Base Station-User Equipment Messaging Regarding Deep Neural Networks

ABSTRACT

Techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. A network entity (base station  121 , core network server  320 ) determines a neural network formation configuration (architecture and/or parameter configurations  1208 ) for a deep neural network (deep neural network(s)  604, 608, 612, 616 ) for processing communications transmitted over the wireless communication system. The network entity (base station  121 , core network server  302 ) communicates the neural network formation configuration to a user equipment (UE  110 ). The user equipment (UE  110 ) configures a first neural network (deep neural network(s)  608, 612 ) based on the neural network formation configuration. In implementations, the user equipment (UE  110 ) recovers information communicated over the wireless network using the first neural network (deep neural network(s)  608, 612 ). This allows the wireless communication system to adapt to changing operating conditions and improve information recovery.

BACKGROUND

The evolution of wireless communication systems oftentimes stems from ademand for data throughput. As one example, the demand for dataincreases as more and more devices gain access to the wirelesscommunication system. Evolving devices also execute data-intensiveapplications that utilize more data than traditional applications, suchas data-intensive streaming video applications, data-intensive socialmedia applications, data-intensive audio services, etc. This increaseddemand can, at times, deplete the data resources of the wirelesscommunication system. Thus, to accommodate increased data usage,evolving wireless communication systems utilize increasingly complexarchitectures as a way to provide more data throughput relative tolegacy wireless communication systems.

As one example, fifth generation (5G) standards and technologiestransmit data using higher frequency ranges, such as the above-6Gigahertz (GHz) band, to increase data capacity. However, transmittingand recovering information using these higher frequency ranges poseschallenges. To illustrate, higher frequency signals are more susceptibleto multipath fading, scattering, atmospheric absorption, diffraction,interference, and so forth, relative to lower frequency signals. Thesesignal distortions oftentimes lead to errors when recovering theinformation at a receiver. As another example, hardware capable oftransmitting, receiving, routing, and/or otherwise using these higherfrequencies can be complex and expensive, which increases the processingcosts in a wirelessly-networked device.

SUMMARY

This document describes techniques and apparatuses for base station-userequipment messaging regarding deep neural networks (DNNs). In someaspects, a network entity determines a neural network (NN) formationconfiguration for a DNN for processing communications transmitted over awireless communication system, where the NN formation configurationspecifies any combination of neural network architecture configurationsand/or parameter configurations as further described. The networkentity, in implementations, generates a message that includes anindication of the NN formation configuration for the DNN. In turn, thenetwork entity transmits the message to a user equipment to direct theuser equipment to form the DNN using the neural network formationconfiguration and to process the communications transmitted over thewireless communication system using the DNN. By doing so, the networkentity can monitor changing operating conditions in the wirelesscommunication system and determine the DNN based on the changingoperating conditions, thus improving information recovery in thewireless communication system.

Some aspects of communicating a neural network formation configuration(NN formation configuration) utilize a neural network table. A networkentity in a wireless communication system transmits, to a userequipment, a neural network table that includes a plurality of NNformation configuration elements, each NN formation configurationelement of the plurality of NN formation configuration elementsconfiguring at least a portion of a DNN for processing communicationstransmitted over the wireless communication system. The network entitythen selects one or more neural network formation configuration elementsfrom the plurality of NN formation configuration elements to create a NNformation configuration, and transmits an indication to the userequipment to direct the user equipment to form a deep neural networkusing the neural network formation configuration and to process thecommunications using the deep neural network. In an example arrangement,the network entity determines index value(s) of the neural network tablethat corresponds to the selected NN formation configuration elements. Inturn, the network entity transmits the index value(s) to the userequipment to direct the user equipment to form a DNN using the NNformation configuration and to process the communications using the DNN.By communicating a neural network table to a user equipment, the networkentity can quickly reconfigure a DNN at the user equipment as operatingconditions change, such as by transmitting an index value to the neuralnetwork table rather than transmitting individual parameterconfigurations, such as, by way of example and not of limitation,pooling parameter(s), kernel parameter(s), weights, and/or layerparameter(s), etc., to configure a DNN. This reduces data transmissionsbetween the network entity and user equipment, improves theresponsiveness of the user equipment, and improves information recoveryin the wireless communication system.

In some aspects, a user equipment associated with a wirelesscommunication system receives a message that indicates a NN formationconfiguration for a DNN for processing communications transmitted overthe wireless communication system. In turn, the user equipment forms theDNN using the NN formation configuration indicated in the message. Theuser equipment receives the communications from a base station, andprocesses the communications using the DNN to extract informationtransmitted in the communications. By configuring the DNN based on inputfrom a network entity, the user equipment can improve informationrecovery in the wireless communication system.

In some aspects, a user equipment in a wireless communication systemreceives a neural network table that includes a plurality of NNformation configuration elements that provide the user equipment with anability to configure a DNN for processing communications transmittedover the wireless communication system. The user equipment receives amessage that directs the user equipment to form the DNN using a NNformation configuration base on one or more neural network formationconfiguration elements in the plurality of NN formation configurationelements. The user equipment forms the DNN with the NN formationconfiguration by accessing the neural network table to obtain the one ormore NN formation configuration elements, and processes thecommunications transmitted over the wireless communication system usingthe DNN. By receiving, storing, and accessing a neural network table toconfigure a DNN, the user equipment can quickly respond to directions toreconfigure the DNN and improve information recovery in the wirelesscommunication system.

The details of one or more implementations of base station-userequipment messaging using DNNs, and communicating neural networkformation configurations, are set forth in the accompanying drawings andthe following description. Other features and advantages will beapparent from the description and drawings, and from the claims. Thissummary is provided to introduce subject matter that is furtherdescribed in the Detailed Description and Drawings. Accordingly, thissummary should not be considered to describe essential features nor usedto limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more aspects of base station-user equipmentmessaging regarding deep neural networks and communicating neuralnetwork formation configurations are described below. The use of thesame reference numbers in different instances in the description and thefigures indicate similar elements:

FIG. 1 illustrates an example environment in which various aspects ofbase station-user equipment messaging using deep neural networks can beimplemented.

FIG. 2 illustrates an example device diagram of devices that canimplement various aspects of base station-user equipment messaging usingdeep neural networks.

FIG. 3 illustrates an example device diagram of a device that canimplement various aspects of base station-user equipment messaging usingdeep neural networks.

FIG. 4 illustrates an example machine-learning module that can implementvarious aspects of base station-user equipment messaging using deepneural networks.

FIG. 5 illustrates example block diagrams of processing chains utilizedby devices to process communications transmitted over a wirelesscommunication system.

FIG. 6 illustrates an example operating environment in which multipledeep neural networks are utilized in a wireless communication system.

FIG. 7 illustrates an example transaction diagram between variousdevices for configuring a neural network using a neural networkformation configuration.

FIG. 8 illustrates an example transaction diagram between variousdevices for updating a neural network using a neural network formationconfiguration.

FIGS. 9-1 and 9-2 illustrate an example transaction diagram betweenvarious devices for configuring a neural network using a neural networkformation configuration.

FIG. 10 illustrates an example method for configuring a neural networkfor processing communications transmitted over a wireless communicationsystem.

FIG. 11 illustrates an example method for forming a neural network basedon a neural network formation configuration.

FIG. 12 illustrates an example of generating multiple neural networkformation configurations.

FIG. 13 illustrates an example transaction diagram between variousdevices for communicating neural network formation configurations.

FIG. 14 illustrates an example transaction diagram between variousnetwork entities for communicating neural network formationconfigurations.

FIG. 15 illustrates an example transaction diagram between variousnetwork entities for communicating neural network formationconfigurations.

FIGS. 16-1 and 16-2 illustrate an example environment for communicatingneural network formation configurations using a set of candidate neuralnetwork formation configurations.

FIG. 17 illustrates an example transaction diagram between variousnetwork entities for communicating neural network formationconfigurations.

FIG. 18 illustrates an example method for communicating neural networkformation configurations over a wireless communication system.

FIG. 19 illustrates an example method for communicating neural networkformation configurations over a wireless communication system.

DETAILED DESCRIPTION

In conventional wireless communication systems, transmitter and receiverprocessing chains include complex functionality. For instance, a channelestimation block in the processing chain estimates or predicts how atransmission environment distorts a signal propagating through thetransmission environment. As another example, channel equalizer blocksreverse the distortions identified by the channel estimation block fromthe signal. These complex functions oftentimes become more complicatedwhen processing higher frequency ranges, such as 5G frequencies at oraround the 6 GHz range. For instance, transmission environments add moredistortion to the higher frequency ranges relative to lower frequencyranges, thus making information recovery more complex. As anotherexample, hardware capable of processing and routing the higher frequencyranges oftentimes adds increased costs and complex physical constraints.

DNNs provide solutions to complex processing, such as the complexfunctionality used in a wireless communication system. By training a DNNon transmitter and/or receiver processing chain operations, the DNN canreplace the conventional complex functionality in a variety of ways,such as by replacing some or all of the conventional processing blocksused in end-to-end processing of wireless communication signals,replacing individual processing chain blocks, etc. Dynamicreconfiguration of a DNN, such as by modifying various parameterconfigurations (e.g., coefficients, layer connections, kernel sizes)also provides an ability to adapt to changing operating conditions.

This document describes aspects of base station-user equipment messagingregarding DNNs, and communicating NN formation configurations, which maybe used to process communications in a wireless communication system,and dynamically reconfigure the DNNs as operating conditions change.Aspects can be implemented by network entities that operate in thewireless communication system (e.g., a base station, a core networkserver, a user equipment).

For example, a network entity determines a NN formation configurationfor a DNN for processing communications transmitted over the wirelesscommunication system. Here, the phrase “transmitted over” includesgenerating communications to be transmitted over the wirelesscommunication system (e.g. processing pre-transmission communications)and/or processing communications received over the wirelesscommunication system. Thus, “processing communications transmitted overthe wireless communication system” includes generating thetransmissions, processing received transmissions, or any combinationthereof. The network entity, in implementations, generates a messagethat includes an indication of the NN formation configuration for theDNN. The network entity then transmits the message to a user equipmentto direct the user equipment to form the DNN using the NN formationconfiguration, and to process the communications transmitted over thewireless communication system using the DNN. By doing so, the networkentity can monitor changing operating conditions in the wirelesscommunication system, and update the neural networks based on thechanging operating conditions, thus improving information recovery inthe wireless communication system.

In some implementations, a network entity in a wireless communicationsystem transmits, to a user equipment, a neural network table thatincludes a plurality of NN formation configuration elements, each NNformation configuration element of the plurality of NN formationconfiguration elements configuring at least a portion of a DNN forprocessing communications transmitted over the wireless communicationsystem. The network entity then selects one or more neural networkformation configuration elements from the plurality of neural networkformation configuration elements to create a neural network formationconfiguration, and transmits an indication to the user equipment todirect the user equipment to form a DNN using the NN formationconfiguration and to process the communications using the DNN.

In an example arrangement, the network entity determines index value(s)of the neural network table that corresponds to the selected NNformation configuration elements. In turn, the network entity transmitsthe index value(s) to the user equipment to direct the user equipment toform a DNN using the NN formation configuration, and to process thecommunications using the DNN. By communicating a neural network table toa user equipment, the network entity can quickly reconfigure a DNN atthe user equipment as operating conditions change, such as bytransmitting an index value of the neural network table to the userequipment, rather than transmitting parameter configurations toreconfigure the DNN. This reduces data transmissions between the networkentity and user equipment, improves the responsiveness of the userequipment, and improves information recovery in the wirelesscommunication system.

In some aspects, a user equipment associated with a wirelesscommunication system receives a message that indicates a NN formationconfiguration for a DNN for processing communications transmitted overthe wireless communication system. In turn, the user equipment forms theDNN using the NN formation configuration indicated in the message. Asthe user equipment receives the communications from a base station, theuser equipment extracts information transmitted in the communications byprocessing the communications using the DNN. By configuring the DNNbased on input from the network entity, the user equipment can improveinformation recovery in the wireless communication system.

In some aspects, a user equipment in a wireless communication systemreceives a neural network table that includes a plurality of NNformation configuration elements that provide the user equipment with anability to configure a DNN for processing communications transmittedover the wireless communication system. The user equipment receives amessage that directs the user equipment to form the DNN using a NNformation configuration based on one or more NN formation configurationelements in the plurality of NN formation configuration elements. Theuser equipment then forms the DNN by accessing the neural network tableto obtain the NN formation configuration (e.g., the NN formationconfiguration elements), and processes the communications transmittedover the wireless communication system using the DNN. By receiving,storing, and accessing the neural network table to configure the DNN,the user equipment can quickly respond to directions to reconfigure theDNN and improve information recovery in the wireless communicationsystem.

Example Environments

FIG. 1 illustrates an example environment 100 which includes a userequipment 110 (UE 110) that can communicate with base stations 120(illustrated as base stations 121 and 122) through one or more wirelesscommunication links 130 (wireless link 130), illustrated as wirelesslinks 131 and 132. For simplicity, the UE 110 is implemented as asmartphone but may be implemented as any suitable computing orelectronic device, such as a mobile communication device, modem,cellular phone, gaming device, navigation device, media device, laptopcomputer, desktop computer, tablet computer, smart appliance,vehicle-based communication system, or an Internet-of-Things (IoT)device such as a sensor or an actuator. The base stations 120 (e.g., anEvolved Universal Terrestrial Radio Access Network Node B, E-UTRAN NodeB, evolved Node B, eNodeB, eNB, Next Generation Node B, gNode B, gNB,ng-eNB, or the like) may be implemented in a macrocell, microcell, smallcell, picocell, and the like, or any combination thereof.

The base stations 120 communicate with the user equipment 110 using thewireless links 131 and 132, which may be implemented as any suitabletype of wireless link. The wireless links 131 and 132 include controland data communication, such as downlink of data and control informationcommunicated from the base stations 120 to the user equipment 110,uplink of other data and control information communicated from the userequipment 110 to the base stations 120, or both. The wireless links 130may include one or more wireless links (e.g., radio links) or bearersimplemented using any suitable communication protocol or standard, orcombination of communication protocols or standards, such as 3rdGeneration Partnership Project Long-Term Evolution (3GPP LTE), FifthGeneration New Radio (5G NR), and so forth. Multiple wireless links 130may be aggregated in a carrier aggregation to provide a higher data ratefor the UE 110. Multiple wireless links 130 from multiple base stations120 may be configured for Coordinated Multipoint (CoMP) communicationwith the UE 110.

The base stations 120 are collectively a Radio Access Network 140 (e.g.,RAN, Evolved Universal Terrestrial Radio Access Network, E-UTRAN, 5G NRRAN or NR RAN). The base stations 121 and 122 in the RAN 140 areconnected to a core network 150. The base stations 121 and 122 connect,at 102 and 104 respectively, to the core network 150 through an NG2interface for control-plane signaling and using an NG3 interface foruser-plane data communications when connecting to a 5G core network, orusing an S1 interface for control-plane signaling and user-plane datacommunications when connecting to an Evolved Packet Core (EPC) network.The base stations 121 and 122 can communicate using an Xn ApplicationProtocol (XnAP) through an Xn interface, or using an X2 ApplicationProtocol (X2AP) through an X2 interface, at 106, to exchange user-planeand control-plane data. The user equipment 110 may connect, via the corenetwork 150, to public networks, such as the Internet 160 to interactwith a remote service 170.

Example Devices

FIG. 2 illustrates an example device diagram 200 of the user equipment110 and one of the base stations 120. FIG. 3 illustrates an exampledevice diagram 300 of a core network server 302. The user equipment 110,the base station 120, and/or the core network server 302 may includeadditional functions and interfaces that are omitted from FIG. 2 or FIG.3 for the sake of clarity.

The user equipment 110 includes antennas 202, a radio frequency frontend 204 (RF front end 204), a wireless transceiver (e.g., an LTEtransceiver 206, and/or a 5G NR transceiver 208) for communicating withthe base station 120 in the RAN 140. The RF front end 204 of the userequipment 110 can couple or connect the LTE transceiver 206, and the 5GNR transceiver 208 to the antennas 202 to facilitate various types ofwireless communication. The antennas 202 of the user equipment 110 mayinclude an array of multiple antennas that are configured similar to ordifferently from each other. The antennas 202 and the RF front end 204can be tuned to, and/or be tunable to, one or more frequency bandsdefined by the 3GPP LTE and 5G NR communication standards andimplemented by the LTE transceiver 206, and/or the 5G NR transceiver208. Additionally, the antennas 202, the RF front end 204, the LTEtransceiver 206, and/or the 5G NR transceiver 208 may be configured tosupport beamforming for the transmission and reception of communicationswith the base station 120. By way of example and not limitation, theantennas 202 and the RF front end 204 can be implemented for operationin sub-gigahertz bands, sub-6 GHZ bands, and/or above 6 GHz bands thatare defined by the 3GPP LTE and 5G NR communication standards.

The user equipment 110 also includes processor(s) 210 andcomputer-readable storage media 212 (CRM 212). The processor 210 may bea single core processor or a multiple core processor composed of avariety of materials, such as silicon, polysilicon, high-K dielectric,copper, and so on. The computer-readable storage media described hereinexcludes propagating signals. CRM 212 may include any suitable memory orstorage device such as random-access memory (RAM), static RAM (SRAM),dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), orFlash memory useable to store device data 214 of the user equipment 110.The device data 214 includes user data, multimedia data, beamformingcodebooks, applications, neural network tables, and/or an operatingsystem of the user equipment 110, which are executable by processor(s)210 to enable user-plane communication, control-plane signaling, anduser interaction with the user equipment 110.

In some implementations, the computer-readable storage media 212includes a neural network table 216 that stores various architectureand/or parameter configurations that form a neural network, such as, byway of example and not of limitation, parameters that specify a fullyconnected layer neural network architecture, a convolutional layerneural network architecture, a recurrent neural network layer, a numberof connected hidden neural network layers, an input layer architecture,an output layer architecture, a number of nodes utilized by the neuralnetwork, coefficients (e.g., weights and biases) utilized by the neuralnetwork, kernel parameters, a number of filters utilized by the neuralnetwork, strides/pooling configurations utilized by the neural network,an activation function of each neural network layer, interconnectionsbetween neural network layers, neural network layers to skip, and soforth. Accordingly, the neural network table 216 includes anycombination of NN formation configuration elements (e.g., architectureand/or parameter configurations) that can be used to create a NNformation configuration (e.g., a combination of one or more NN formationconfiguration elements) that defines and/or forms a DNN. In someimplementations, a single index value of the neural network table 216maps to a single NN formation configuration element (e.g., a 1:1correspondence). Alternately or additionally, a single index value ofthe neural network table 216 maps to a NN formation configuration (e.g.,a combination of NN formation configuration elements). In someimplementations, the neural network table includes input characteristicsfor each NN formation configuration element and/or NN formationconfiguration, where the input characteristics describe properties aboutthe training data used to generate the NN formation configurationelement and/or NN formation configuration as further described.

In some implementations, the CRM 212 may also include a user equipmentneural network manager 218 (UE neural network manager 218). Alternatelyor additionally, the UE neural network manager 218 may be implemented inwhole or part as hardware logic or circuitry integrated with or separatefrom other components of the user equipment 110. The UE neural networkmanager 218 accesses the neural network table 216, such as by way of anindex value, and forms a DNN using the NN formation configurationelements specified by a NN formation configuration. In implementations,UE neural network manager forms multiple DNNs to process wirelesscommunications (e.g., downlink communications and/or uplinkcommunications exchanged with the base station 120).

The device diagram for the base station 120, shown in FIG. 2, includes asingle network node (e.g., a gNode B). The functionality of the basestation 120 may be distributed across multiple network nodes or devicesand may be distributed in any fashion suitable to perform the functionsdescribed herein. The base station 120 include antennas 252, a radiofrequency front end 254 (RF front end 254), one or more wirelesstransceivers (e.g. one or more LTE transceivers 256, and/or one or more5G NR transceivers 258) for communicating with the UE 110. The RF frontend 254 of the base station 120 can couple or connect the LTEtransceivers 256 and the 5G NR transceivers 258 to the antennas 252 tofacilitate various types of wireless communication. The antennas 252 ofthe base station 120 may include an array of multiple antennas that areconfigured similar to, or different from, each other. The antennas 252and the RF front end 254 can be tuned to, and/or be tunable to, one ormore frequency band defined by the 3GPP LTE and 5G NR communicationstandards, and implemented by the LTE transceivers 256, and/or the 5G NRtransceivers 258. Additionally, the antennas 252, the RF front end 254,the LTE transceivers 256, and/or the 5G NR transceivers 258 may beconfigured to support beamforming, such as Massive-MIMO, for thetransmission and reception of communications with the UE 110.

The base station 120 also include processor(s) 260 and computer-readablestorage media 262 (CRM 262). The processor 260 may be a single coreprocessor or a multiple core processor composed of a variety ofmaterials, such as silicon, polysilicon, high-K dielectric, copper, andso on. CRM 262 may include any suitable memory or storage device such asrandom-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM),non-volatile RAM (NVRAM), read-only memory (ROM), or Flash memoryuseable to store device data 264 of the base station 120. The devicedata 264 includes network scheduling data, radio resource managementdata, beamforming codebooks, applications, and/or an operating system ofthe base station 120, which are executable by processor(s) 260 to enablecommunication with the user equipment 110.

CRM 262 also includes a base station manager 266. Alternately oradditionally, the base station manager 266 may be implemented in wholeor part as hardware logic or circuitry integrated with or separate fromother components of the base station 120. In at least some aspects, thebase station manager 266 configures the LTE transceivers 256 and the 5GNR transceivers 258 for communication with the user equipment 110, aswell as communication with a core network, such as the core network 150.

CRM 262 also includes a base station neural network manager 268 (BSneural network manager 268). Alternately or additionally, the BS neuralnetwork manager 268 may be implemented in whole or part as hardwarelogic or circuitry integrated with or separate from other components ofthe base station 120. In at least some aspects, the BS neural networkmanager 268 selects the NN formation configurations utilized by the basestation 120 and/or UE 110 to configure deep neural networks forprocessing wireless communications, such as by selecting a combinationof NN formation configuration elements. In some implementations, the BSneural network manager receives feedback from the UE 110, and selectsthe neural network formation configuration based on the feedback.Alternately or additionally, the BS neural network manager 268 receivesneural network formation configuration directions from core network 150elements through a core network interface 276 or an inter-base stationinterface 274 and forwards the neural network formation configurationdirections to UE 110.

CRM 262 includes training module 270 and neural network table 272. Inimplementations, the base station 120 manage and deploy NN formationconfigurations to UE 110. Alternately or additionally, the base station120 maintain the neural network table 272. The training module 270teaches and/or trains DNNs using known input data. For instance, thetraining module 270 trains DNN(s) for different purposes, such asprocessing communications transmitted over a wireless communicationsystem (e.g., encoding downlink communications, modulating downlinkcommunications, demodulating downlink communications, decoding downlinkcommunications, encoding uplink communications, modulating uplinkcommunications, demodulating uplink communications, decoding uplinkcommunications). This includes training the DNN(s) offline (e.g., whilethe DNN is not actively engaged in processing the communications) and/oronline (e.g., while the DNN is actively engaged in processing thecommunications).

In implementations, the training module 270 extracts learned parameterconfigurations from the DNN to identify the NN formation configurationelements and/or NN formation configuration, and then adds and/or updatesthe NN formation configuration elements and/or NN formationconfiguration in the neural network table 272. The extracted parameterconfigurations include any combination of information that defines thebehavior of a neural network, such as node connections, coefficients,active layers, weights, biases, pooling, etc.

The neural network table 272 stores multiple different NN formationconfiguration elements and/or NN formation configurations generatedusing the training module 270. In some implementations, the neuralnetwork table includes input characteristics for each NN formationconfiguration element and/or NN formation configuration, where the inputcharacteristics describe properties about the training data used togenerate the NN formation configuration element and/or NN formationconfiguration. For instance, the input characteristics includes, by wayof example and not of limitation, power information,signal-to-interference-plus-noise ratio (SINR) information, channelquality indicator (CQI) information, channel state information (CSI),Doppler feedback, frequency bands, BLock Error Rate (BLER), Quality ofService (QoS), Hybrid Automatic Repeat reQuest (HARQ) information (e.g.,first transmission error rate, second transmission error rate, maximumretransmissions), latency, Radio Link Control (RLC), Automatic RepeatreQuest (ARQ) metrics, received signal strength (RSS), uplink SINR,timing measurements, error metrics, UE capabilities, BS capabilities,power mode, Internet Protocol (IP) layer throughput, end2end latency,end2end packet loss ratio, etc. Accordingly, the input characteristicsinclude, at times, Layer 1, Layer 2, and/or Layer 3 metrics. In someimplementations, a single index value of the neural network table 272maps to a single NN formation configuration element (e.g., a 1:1correspondence). Alternately or additionally, a single index value ofthe neural network table 272 maps to a NN formation configuration (e.g.,a combination of NN formation configuration elements).

In implementations, the base station 120 synchronizes the neural networktable 272 with the neural network table 216 such that the NN formationconfiguration elements and/or input characteristics stored in one neuralnetwork table is replicated in the second neural network table.Alternately or additionally, the base station 120 synchronizes theneural network table 272 with the neural network table 216 such that theNN formation configuration elements and/or input characteristics storedin one neural network table represent complementary functionality in thesecond neural network table (e.g., NN formation configuration elementsfor transmitter path processing in the first neural network table, NNformation configuration elements for receiver path processing in thesecond neural network table).

The base station 120 also include an inter-base station interface 274,such as an Xn and/or X2 interface, which the base station manager 266configures to exchange user-plane, control-plane, and other informationbetween other base station 120, to manage the communication of the basestation 120 with the user equipment 110. The base station 120 include acore network interface 276 that the base station manager 266 configuresto exchange user-plane, control-plane, and other information with corenetwork functions and/or entities.

In FIG. 3, the core network server 302 may provide all or part of afunction, entity, service, and/or gateway in the core network 150. Eachfunction, entity, service, and/or gateway in the core network 150 may beprovided as a service in the core network 150, distributed acrossmultiple servers, or embodied on a dedicated server. For example, thecore network server 302 may provide the all or a portion of the servicesor functions of a User Plane Function (UPF), an Access and MobilityManagement Function (AMF), a Serving Gateway (S-GW), a Packet DataNetwork Gateway (P-GW), a Mobility Management Entity (MME), an EvolvedPacket Data Gateway (ePDG), and so forth. The core network server 302 isillustrated as being embodied on a single server that includesprocessor(s) 304 and computer-readable storage media 306 (CRM 306). Theprocessor 304 may be a single core processor or a multiple coreprocessor composed of a variety of materials, such as silicon,polysilicon, high-K dielectric, copper, and so on. CRM 306 may includeany suitable memory or storage device such as random-access memory(RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM),read-only memory (ROM), hard disk drives, or Flash memory useful tostore device data 308 of the core network server 302. The device data308 includes data to support a core network function or entity, and/oran operating system of the core network server 302, which are executableby processor(s) 304.

CRM 306 also includes one or more core network applications 310, which,in one implementation, is embodied on CRM 306 (as shown). The one ormore core network applications 310 may implement the functionality suchas UPF, AMF, S-GW, P-GW, MME, ePDG, and so forth. Alternately oradditionally, the one or more core network applications 310 may beimplemented in whole or part as hardware logic or circuitry integratedwith or separate from other components of the core network server 302.

CRM 306 also includes a core network neural network manager 312 thatmanages NN formation configurations used to process communicationsexchanged between UE 110 and the base stations 120. In someimplementations, the core network neural network manager 312 analyzesvarious parameters, such as current signal channel conditions (e.g., asreported by base stations 120, as reported by other wireless accesspoints, as reported by UEs 110 (via base stations or other wirelessaccess points)), capabilities at base stations 120 (e.g., antennaconfigurations, cell configurations, MIMO capabilities, radiocapabilities, processing capabilities), capabilities of UE 110 (e.g.,antenna configurations, MIMO capabilities, radio capabilities,processing capabilities), and so forth. For example, the base stations120 obtain the various parameters during the communications with the UEand forward the parameters to the core network neural network manager312. The core network neural network manager selects, based on theseparameters, a NN formation configuration that improves the accuracy of aDNN processing the communications. Improving the accuracy signifies animproved accuracy in the output, such as lower bit errors, generated bythe neural network relative to a neural network configured with anotherNN formation configuration. The core network neural network manager 312then communicates the selected NN formation configuration to the basestations 120 and/or the UE 110. In implementations, the core networkneural network manager 312 receives UE and/or BS feedback from the basestation 120 and selects an updated NN formation configuration based onthe feedback.

CRM 306 includes training module 314 and neural network table 316. Inimplementations, the core network server 302 manages and deploys NNformation configurations to multiple devices in a wireless communicationsystem, such as UEs 110 and base stations 120. Alternately oradditionally, the core network server maintains the neural network table316 outside of the CRM 306. The training module 314 teaches and/ortrains DNNs using known input data. For instance, the training module314 trains DNN(s) to process different types of pilot communicationstransmitted over a wireless communication system. This includes trainingthe DNN(s) offline and/or online. In implementations, the trainingmodule 314 extracts a learned NN formation configuration and/or learnedNN formation configuration elements from the DNN and stores the learnedNN formation configuration elements in the neural network table 316.Thus, a NN formation configuration includes any combination ofarchitecture configurations (e.g., node connections, layer connections)and/or parameter configurations (e.g., weights, biases, pooling) thatdefine or influence the behavior of a DNN. In some implementations, asingle index value of the neural network table 316 maps to a single NNformation configuration element (e.g., a 1:1 correspondence).Alternately or additionally, a single index value of the neural networktable 316 maps to a NN formation configuration (e.g., a combination ofNN formation configuration elements).

In some implementations, the training module 314 of the core networkneural network manager 312 generates complementary NN formationconfigurations and/or NN formation configuration elements to thosestored in the neural network table 216 at the UE 110 and/or the neuralnetwork table 272 at the base station 121. As one example, the trainingmodule 314 generates neural network table 316 with NN formationconfigurations and/or NN formation configuration elements that have ahigh variation in the architecture and/or parameter configurationsrelative to medium and/or low variations used to generate the neuralnetwork table 272 and/or the neural network table 216. For instance, theNN formation configurations and/or NN formation configuration elementsgenerated by the training module 314 correspond to fully connectedlayers, a full kernel size, frequent sampling and/or pooling, highweighting accuracy, and so forth. Accordingly, the neural network table316 includes, at times, high accuracy neural networks at the trade-offof increased processing complexity and/or time.

The NN formation configurations and/or NN formation configurationelements generated by the training module 270 have, at times, more fixedarchitecture and/or parameter configurations (e.g., fixed connectionlayers, fixed kernel size, etc.), and less variation, relative to thosegenerated by the training module 314. The training module 270, forexample, generates streamlined NN formation configurations (e.g., fastercomputation times, less data processing), relative to those generated bythe training module 314, to optimize or improve a performance of end2endnetwork communications at the base station 121 and/or the UE 110.Alternately or additionally, the NN formation configurations and/or NNformation configuration elements stored at the neural network table 216at the UE 110 include more fixed architecture and/or parameterconfigurations, relative to those stored in the neural network table 316and/or the neural network table 272, that reduce requirements (e.g.,computation speed, less data processing points, less computations, lesspower consumption, etc.) at the UE 110 relative to the base station 121and/or the core network server 302. In implementations, the variationsin fixed (or flexible) architecture and/or parameter configurations ateach neural network are based on the processing resources (e.g.,processing capabilities, memory constraints, quantization constraints(e.g., 8-bit vs. 16-bit), fixed point vs floating point computations,power availability) of the devices targeted to form the correspondingDNNs. Thus, UEs or access points with less processing resources relativeto a core network server or base station receive NN formationconfigurations optimized for the available processing resources.

The neural network table 316 stores multiple different NN formationconfiguration elements generated using the training module 314. In someimplementations, the neural network table includes input characteristicsfor each NN formation configuration element and/or NN formationconfiguration, where the input characteristics describe properties aboutthe training data used to generate the NN formation configuration. Forinstance, the input characteristics can include power information, SINRinformation, CQI, CSI, Doppler feedback, RSS, error metrics, minimumend2end (E2E) latency, desired E2E latency, E2E QoS, E2E throughput, E2Epacket loss ratio, cost of service, etc.

The core network server 302 also includes a core network interface 318for communication of user-plane, control-plane, and other informationwith the other functions or entities in the core network 150, basestations 120, or UE 110. In implementations, the core network server 302communicates NN formation configurations to the base station 120 usingthe core network interface 318. The core network server 302 alternatelyor additionally receives feedback from the base stations 120 and/or theUE 110, by way of the base stations 120, using the core networkinterface 318.

Having described an example environment and example devices that can beutilized for base station-user equipment messaging using deep neuralnetworks, consider now a discussion of configurable machine-learningmodules that is in accordance with one or more implementations.

Configurable Machine-Learning Modules

FIG. 4 illustrates an example machine-learning module 400. Themachine-learning module 400 implements a set of adaptive algorithms thatlearn and identify patterns within data. The machine-learning module 400can be implemented using any combination of software, hardware, and/orfirmware.

In FIG. 4, the machine-learning module 400 includes a deep neuralnetwork 402 (DNN 402) with groups of connected nodes (e.g., neuronsand/or perceptrons) that are organized into three or more layers. Thenodes between layers are configurable in a variety of ways, such as apartially-connected configuration where a first subset of nodes in afirst layer are connected with a second subset of nodes in a secondlayer, a fully-connected configuration where each node in a first layerare connected to each node in a second layer, etc. A neuron processesinput data to produce a continuous output value, such as any real numberbetween 0 and 1. In some cases, the output value indicates how close theinput data is to a desired category. A perceptron performs linearclassifications on the input data, such as a binary classification. Thenodes, whether neurons or perceptrons, can use a variety of algorithmsto generate output information based upon adaptive learning. Using theDNN, the machine-learning module 400 performs a variety of differenttypes of analysis, including single linear regression, multiple linearregression, logistic regression, step-wise regression, binaryclassification, multiclass classification, multi-variate adaptiveregression splines, locally estimated scatterplot smoothing, and soforth.

In some implementations, the machine-learning module 400 adaptivelylearns based on supervised learning. In supervised learning, themachine-learning module receives various types of input data as trainingdata. The machine-learning module processes the training data to learnhow to map the input to a desired output. As one example, themachine-learning module 400 receives digital samples of a signal asinput data and learns how to map the signal samples to binary data thatreflects information embedded within the signal. As another example, themachine-learning module 400 receives binary data as input data andlearns how to map the binary data to digital samples of a signal withthe binary data embedded within the signal. During a training procedure,the machine-learning module 400 uses labeled or known data as an inputto the DNN. The DNN analyzes the input using the nodes and generates acorresponding output. The machine-learning module 400 compares thecorresponding output to truth data and adapts the algorithms implementedby the nodes to improve the accuracy of the output data. Afterwards, theDNN applies the adapted algorithms to unlabeled input data to generatecorresponding output data.

The machine-learning module 400 uses statistical analyses and/oradaptive learning to map an input to an output. For instance, themachine-learning module 400 uses characteristics learned from trainingdata to correlate an unknown input to an output that is statisticallylikely within a threshold range or value. This allows themachine-learning module 400 to receive complex input and identify acorresponding output. Some implementations train the machine-learningmodule 400 on characteristics of communications transmitted over awireless communication system (e.g., time/frequency interleaving,time/frequency deinterleaving, convolutional encoding, convolutionaldecoding, power levels, channel equalization, inter-symbol interference,quadrature amplitude modulation/demodulation, frequency-divisionmultiplexing/de-multiplexing, transmission channel characteristics).This allows the trained machine-learning module 400 to receive samplesof a signal as an input, such as samples of a downlink signal receivedat a user equipment, and recover information from the downlink signal,such as the binary data embedded in the downlink signal.

In FIG. 4, the DNN includes an input layer 404, an output layer 406, andone or more hidden layer(s) 408 that are positioned between the inputlayer and the output layer. Each layer has an arbitrary number of nodes,where the number of nodes between layers can be the same or different.In other words, input layer 404 can have a same number and/or differentnumber of nodes as output layer 406, output layer 406 can have a samenumber and/or different number of nodes than hidden layer(s) 408, and soforth.

Node 410 corresponds to one of several nodes included in input layer404, where the nodes perform independent computations from one another.As further described, a node receives input data, and processes theinput data using algorithm(s) to produce output data. At times, thealgorithm(s) include weights and/or coefficients that change based onadaptive learning. Thus, the weights and/or coefficients reflectinformation learned by the neural network. Each node can, in some cases,determine whether to pass the processed input data to the next node(s).To illustrate, after processing input data, node 410 can determinewhether to pass the processed input data to node 412 and/or node 414 ofhidden layer(s) 408. Alternately or additionally, node 410 passes theprocessed input data to nodes based upon a layer connectionarchitecture. This process can repeat throughout multiple layers untilthe DNN generates an output using the nodes of output layer 406.

A neural network can also employ a variety of architectures thatdetermine what nodes within the neural network are connected, how datais advanced and/or retained in the neural network, what weights andcoefficients are used to process the input data, how the data isprocessed, and so forth. These various factors collectively describe aNN formation configuration. To illustrate, a recurrent neural network,such as a long short-term memory (LSTM) neural network, forms cyclesbetween node connections in order to retain information from a previousportion of an input data sequence. The recurrent neural network thenuses the retained information for a subsequent portion of the input datasequence. As another example, a feed-forward neural network passesinformation to forward connections without forming cycles to retaininformation. While described in the context of node connections, it isto be appreciated that the NN formation configuration can include avariety of parameter configurations that influence how the neuralnetwork processes input data.

A NN formation configuration of a neural network can be characterized byvarious architecture and/or parameter configurations. To illustrate,consider an example in which the DNN implements a convolutional neuralnetwork. Generally, a convolutional neural network corresponds to a typeof DNN in which the layers process data using convolutional operationsto filter the input data. Accordingly, the convolutional NN formationconfiguration can be characterized with, by way of example and not oflimitation, pooling parameter(s), kernel parameter(s), weights, and/orlayer parameter(s).

A pooling parameter corresponds to a parameter that specifies poolinglayers within the convolutional neural network that reduce thedimensions of the input data. To illustrate, a pooling layer can combinethe output of nodes at a first layer into a node input at a secondlayer. Alternately or additionally, the pooling parameter specifies howand where in the layers of data processing the neural network poolsdata. A pooling parameter that indicates “max pooling,” for instance,configures the neural network to pool by selecting a maximum value fromthe grouping of data generated by the nodes of a first layer, and usethe maximum value as the input into the single node of a second layer. Apooling parameter that indicates “average pooling” configures the neuralnetwork to generate an average value from the grouping of data generatedby the nodes of the first layer and use the average value as the inputto the single node of the second layer.

A kernel parameter indicates a filter size (e.g., a width and height) touse in processing input data. Alternately or additionally, the kernelparameter specifies a type of kernel method used in filtering andprocessing the input data. A support vector machine, for instance,corresponds to a kernel method that uses regression analysis to identifyand/or classify data. Other types of kernel methods include Gaussianprocesses, canonical correlation analysis, spectral clustering methods,and so forth. Accordingly, the kernel parameter can indicate a filtersize and/or a type of kernel method to apply in the neural network.

Weight parameters specify weights and biases used by the algorithmswithin the nodes to classify input data. In implementations, the weightsand biases are learned parameter configurations, such as parameterconfigurations generated from training data.

A layer parameter specifies layer connections and/or layer types, suchas a fully-connected layer type that indicates to connect every node ina first layer (e.g., output layer 406) to every node in a second layer(e.g., hidden layer(s) 408), a partially-connected layer type thatindicates which nodes in the first layer to disconnect from the secondlayer, an activation layer type that indicates which filters and/orlayers to activate within the neural network, and so forth. Alternatelyor additionally, the layer parameter specifies types of node layers,such as a normalization layer type, a convolutional layer type, apooling layer type, etc.

While described in the context of pooling parameters, kernel parameters,weight parameters, and layer parameters, it is to be appreciated thatother parameter configurations can be used to form a DNN withoutdeparting from the scope of the claimed subject matter. Accordingly, aNN formation configuration can include any suitable type ofconfiguration parameter that can be applied to a DNN that influences howthe DNN processes input data to generate output data.

Some implementations configure machine-learning module 400 based on acurrent operating environment. To illustrate, consider amachine-learning module trained to generate binary data from digitalsamples of a signal. A transmission environment oftentimes modifies thecharacteristics of a signal traveling through the environment.Transmission environments oftentimes change, which impacts how theenvironment modifies the signal. A first transmission environment, forinstance, modifies a signal in a first manner, while a secondtransmission environment modifies the signal in a different manner thanthe first. These differences impact an accuracy of the output resultsgenerated by a machine-learning module. For instance, a neural networkconfigured to process communications transmitted over the firsttransmission environment may generate errors when processingcommunications transmitted over the second transmission environment(e.g., bit errors that exceed a threshold value).

Various implementations generate and store NN formation configurationsand/or NN formation configuration elements (e.g., various architectureand/or parameter configurations) for different transmissionenvironments. Base stations 120 and/or core network server 302, forexample, train the machine-learning module 400 using any combination ofBS neural network manager 268, training module 270, core network neuralnetwork manager 312, and/or training module 314. The training can occuroffline when no active communication exchanges are occurring, or onlineduring active communication exchanges. For example, the base stations120 and/or core network server 302 can mathematically generate trainingdata, access files that store the training data, obtain real-worldcommunications data, etc. The base stations 120 and/or core networkserver 302 then extract and store the various learned NN formationconfigurations in a neural network table. Some implementations storeinput characteristics with each NN formation configuration, where theinput characteristics describe various properties of the transmissionenvironment corresponding to the respective NN formation configuration.In implementations, a neural network manager selects a NN formationconfiguration and/or NN formation configuration element(s) by matching acurrent transmission environment and/or current operating environment tothe input characteristics.

Having described configurable machine-learning modules, consider now adiscussion of deep neural networks in wireless communication systemsthat is in accordance with one or more implementations.

Deep Neural Networks in Wireless Communication Systems

Wireless communication systems include a variety of complex componentsand/or functions, such as the various devices and modules described withreference to the example environment 100 of FIG. 1, the example devicediagram 200 of FIG. 2, and the example device diagram 300 of FIG. 3. Insome implementations, the devices participating in the wirelesscommunication system chain together a series of functions to enable theexchange of information over wireless connections.

To demonstrate, consider now FIG. 5 that illustrates example blockdiagram 500 and example block diagram 502, each of which depicts anexample processing chain utilized by devices in a wireless communicationsystem. For simplicity, the block diagrams illustrate high-levelfunctionality, and it is to be appreciated that the block diagrams mayinclude additional functions that are omitted from FIG. 5 for the sakeof clarity.

In the upper portion of FIG. 5, block diagram 500 includes a transmitterblock 504 and a receiver block 506. Transmitter block 504 includes atransmitter processing chain that progresses from top to bottom. Thetransmitter processing chain begins with input data that progresses toan encoding stage, followed by a modulating stage, and then a radiofrequency (RF) analog transmit (Tx) stage. The encoding stage caninclude any type and number of encoding stages employed by a device totransmit data over the wireless communication system.

To illustrate, an example encoding stage receives binary data as input,and processes the binary data using various encoding algorithms toappend information to the binary data, such as frame information.Alternately or additionally, the encoding stage transforms the binarydata, such as by applying forward error correction that addsredundancies to help information recovery at a receiver. As anotherexample, the encoding stage converts the binary data into symbols.

An example modulating stage receives an output generated by the encodingstage as input and embeds the input onto a signal. For instance, themodulating stage generates digital samples of signal(s) embedded withthe input from the encoding stage. Thus, in transmitter block 504, theencoding stage and the modulating stage represent a high-leveltransmitter processing chain that oftentimes includes lower-levelcomplex functions, such as convolutional encoding, serial-to-parallelconversion, cyclic prefix insertion, channel coding, time/frequencyinterleaving, and so forth. The RF analog Tx stage receives the outputfrom the modulating stage, generates an analog RF signal based on themodulating stage output, and transmits the analog RF signal to receiverblock 506.

Receiver block 506 performs complementary processing relative totransceiver block 504 using a receiver processing chain. The receiverprocessing chain illustrated in receiver block 506 progresses from topto bottom and includes an RF analog receive (Rx) stage, followed by ademodulating stage, and a decoding stage.

The RF analog Rx stage receives signals transmitted by the transmitterblock 504, and generates an input used by the demodulating stage. As oneexample, the RF analog Rx stage includes a down-conversion componentand/or an analog-to-digital converter (ADC) to generate samples of thereceived signal. The demodulating stage processes input from the RFanalog Rx stage to extract data embedded on the signal (e.g., dataembedded by the modulating stage of the transmitter block 504). Thedemodulating stage, for instance, recovers symbols and/or binary data.

The decoding stage receives input from the demodulating stage, such asrecovered symbols and/or binary data, and processes the input to recoverthe transmitted information. To illustrate, the decoding stage correctsfor data errors based on forward error correction applied at thetransmitter block, extracts payload data from frames and/or slots, andso forth. Thus, the decoding stage generates the recovered information.

As noted, the transmitter and receiver processing chains illustrated bytransmitter block 504 and receiver block 506 have been simplified forclarity and can include multiple complex modules. At times, thesecomplex modules are specific to particular functions and/or conditions.Consider, for example, a receiver processing chain that processesOrthogonal Frequency Division Modulation (OFDM) transmissions. Torecover information from OFDM transmissions, the receiver blockoftentimes includes multiple processing blocks, each of which isdedicated to a particular function, such as an equalization block thatcorrects for distortion in a received signal, a channel estimation blockthat estimates transmission channel properties to identify the effectsof scattering, power decay, and so forth, on a transmission, etc. Athigh frequencies, such as frequencies in the 6 GHz band, these blockscan be computationally and/or monetarily expensive (e.g., requiresubstantial processing power, require expensive hardware). Further,implementing blocks that generate outputs with an accuracy within adesired threshold oftentimes requires more specific and less flexiblecomponents. To illustrate, an equalization block that functions forsignals in the 6 GHz band may not perform with the same accuracy atother frequency bands, thus necessitating different equalization blocksfor different bands and adding complexity to the corresponding devices.

Some implementations include DNNs in the transmission and/or receiverprocessing chains. In block diagram 502, transmitter block 508 includesone or more deep neural network(s) 510 (DNNs 510) in the transmitterprocessing chain, while receiver block 512 includes one or more deepneural network(s) 514 (DNNs 514) in the receiver processing chain.

For simplicity, the DNNs 510 in the transmitter block 508 correspond tothe encoding stage and the modulating stage of transmitter block 504. Itis to be appreciated, however, that the DNNs can perform any high-leveland/or low-level operation found within the transmitter processingchain. For instance, a first DNN performs low-level transmitter-sideforward error correction, a second DNN performs low-leveltransmitter-side convolutional encoding, and so forth. Alternately oradditionally, the DNNs 510 perform high-level processing, such asend-to-end processing that corresponds to the encoding stage and themodulating stage of transmitter block 508.

In a similar manner, the DNNs 514 in receiver block 512 perform receiverprocessing chain functionality (e.g., demodulating stage, decodingstage). The DNNs 514 can perform any high-level and/or low-leveloperation found within the receiver processing chain, such as low-levelreceiver-side bit error correction, low-level receiver-side symbolrecovery, high-level end-to-end demodulating and decoding, etc.Accordingly, DNNs in wireless communication systems can be configured toreplace high-level operations and/or low-level operations in transmitterand receiver processing chains. At times, the DNNs performing thehigh-level operations and/or low-level operations can be configuredand/or reconfigured based on a current operating environment as furtherdescribed. This provides more flexibility and adaptability to theprocessing chains relative to the more specific and less flexiblecomponents.

Some implementations process communication exchanges over the wirelesscommunication system using multiple DNNs, where each DNN has arespective purpose (e.g., uplink processing, downlink processing, uplinkencoding processing, downlink decoding processing, etc.). Todemonstrate, consider now FIG. 6 that illustrates an example operatingenvironment 600 that includes UE 110 and base station 121. The UE 110and base station 121 exchange communications with one another over awireless communication system by processing the communications usingmultiple DNNs.

In FIG. 6, the base station neural network manager 268 of the basestation 121 includes a downlink processing module 602 for processingdownlink communications, such as for generating downlink communicationstransmitted to the UE 110. To illustrate, the base station neuralnetwork manager 268 forms deep neural network(s) 604 (DNNs 604) in thedownlink processing module 602 using NN formation configurations asfurther described. In some examples, the DNNs 604 correspond to the DNNs510 of FIG. 5. In other words, the DNNs 604 perform some or all of thetransmitter processing functionality used to generate downlinkcommunications.

Similarly, the UE neural network manager 218 of the UE 110 includes adownlink processing module 606, where the downlink processing module 606includes deep neural network(s) 608 (DNNs 608) for processing (received)downlink communications. In various implementations, the UE neuralnetwork manager 218 forms the DNNs 608 using NN formationconfigurations. In FIG. 6, the DNNs 608 correspond to the DNNs 514 ofFIG. 5, where the deep neural network(s) 606 of UE 110 perform some orall receiver processing functionality for (received) downlinkcommunications. Accordingly, the DNNs 604 and the DNNs 608 performcomplementary processing to one another (e.g., encoding/decoding,modulating/demodulating).

The DNNs 604 and/or DNNs 608 can include multiple deep neural networks,where each DNN is dedicated to a respective channel, a respectivepurpose, and so forth. The base station 121, as one example, processesdownlink control channel information using a first DNN of the DNNs 604,processes downlink data channel information using a second DNN of theDNNs 604, and so forth. As another example, the UE 110 processesdownlink control channel information using a first DNN of the DNNs 608,processes downlink data channel information using a second DNN of theDNNs 608, etc.

The base station 121 and/or the UE 110 also process uplinkcommunications using DNNs. In environment 600, the UE neural networkmanager 218 includes an uplink processing module 610, where the uplinkprocessing module 610 includes deep neural network(s) 612 (DNNs 612) forgenerating and/or processing uplink communications (e.g., encoding,modulating). In other words, uplink processing module 610 processespre-transmission communications as part of processing the uplinkcommunications. The UE neural network manager 218, for example, formsthe DNNs 612 using NN formation configurations. At times, the DNNs 612correspond to the DNNs 510 of FIG. 5. Thus, the DNNs 612 perform some orall of the transmitter processing functionality used to generate uplinkcommunications transmitted from the UE 110 to the base station 121.

Similarly, uplink processing module 614 of the base station 121 includesdeep neural network(s) 616 (DNNs 616) for processing (received) uplinkcommunications, where base station neural network manager 268 forms DNNs616 using NN formation configurations as further described. In examples,the DNNs 616 of the base station 121 correspond to the DNNs 514 of FIG.5, and perform some or all receiver processing functionality for(received) uplink communications, such as uplink communications receivedfrom UE 110. At times, the DNNs 612 and the DNNs 616 performcomplementary functionality of one another. Alternately or additionally,the uplink processing module 610 and/or the uplink processing module 614include multiple DNNs, where each DNN has a dedicated purpose (e.g.,processes a respective channel, performs respective uplinkfunctionality, and so forth). FIG. 6 illustrates the DNNs 604, 608, 612,and 616 as residing within the respective neural network managers tosignify that the neural network managers form the DNNs, and it is to beappreciated that the DNNs can be formed external to the neural networkmanagers (e.g., UE neural network manager 218 and base station neuralnetwork manager 268) within different components, processing chains,modules, etc.

Having described deep neural networks in wireless communication systems,consider now a discussion of signal and control transactions over awireless communication system that can be used to configure deep neuralnetworks for downlink and uplink communications that is in accordancewith one or more implementations.

Signaling and Control Transactions to Configure Deep Neural Networks

FIGS. 7, 8, and 9 illustrate example signaling and control transactiondiagrams between a base station, a user equipment, and/or a core networkserver in accordance with one or more aspects of base station-userequipment messaging regarding deep neural networks. The signaling andcontrol transactions may be performed by the base station 120 and the UE110 of FIG. 1, or the core network server 302 of FIG. 3, using elementsof FIGS. 1-6. For example, the core network server 302 performs, in someimplementations, various signaling and control actions performed by thebase station 120 as illustrated by FIGS. 7 and 8.

A first example of signaling and control transactions for basestation-user equipment messaging regarding deep neural networks isillustrated by the signaling and control transaction diagram 700 of FIG.7. As illustrated, at 705 the base station 121 determines a neuralnetwork formation configuration. In determining the neural networkformation configuration, the base station analyzes any combination ofinformation, such as a channel type being processed by the deep neuralnetwork (e.g., downlink, uplink, data, control, etc.), transmissionmedium properties (e.g., power measurements,signal-to-interference-plus-noise ratio (SINR) measurements, channelquality indicator (CQI) measurements), encoding schemes, UEcapabilities, BS capabilities, and so forth.

The base station 121, for instance, receives message(s) from the UE 110(not shown) that indicates one or more capabilities of the UE, such as,by way of example and not of limitation, connectivity information,dual-connectivity information, carrier aggregation capabilities,downlink physical parameter values, uplink physical parameter values,supported downlink/uplink categories, inter-frequency handover, etc. Thebase station 121 identifies, from the message(s), the UE capabilitiesthat impact how the UE processes communications, and/or how the basestation processes communications from the UE, and selects a neuralnetwork formation configuration with improved output accuracy relativeto other neural network formation configurations.

In some implementations, the base station 121 selects the neural networkformation configuration from multiple neural network formationconfigurations. Alternately or additionally, the base station 121selects the neural network formation configuration by selecting a subsetof neural network architecture formation elements in a neural networktable. At times, the base station 121 analyzes multiple neural networkformation configurations and/or multiple neural network formationconfiguration elements included in a neural network table, anddetermines the neural network formation configuration by selects and/orcreates a neural network formation configuration that aligns withcurrent channel conditions, such as by matching the channel type,transmission medium properties, etc., to input characteristics asfurther described. Alternately or additionally, the base station 121selects the neural network formation configuration based on networkparameters, such as scheduling parameters (e.g., scheduling MultipleUser, Multiple Input, Multiple Output (MU-MIMO) for downlinkcommunications, scheduling MU-MIMO for uplink communications).

At 710, the base station 121 communicates the neural network formationconfiguration to the UE 110. In some implementations, the base stationtransmits a message that specifies the neural network formationconfiguration, such as by transmitting a message that includes an indexvalue that maps to an entry in a neural network table, such as neuralnetwork table 216 of FIG. 2. Alternately or additionally, the basestation transmits a message that includes neural network parameterconfigurations (e.g., weight values, coefficient values, number offilters). In some cases, the base station 121 specifies a purpose and/orprocessing assignment in the message, where the processing assignmentindicates what channels, and/or where in a processing chain, theconfigured neural network applies to, such as a downlink control channelprocessing, an uplink data channel processing, downlink decodingprocessing, uplink encoding processing, etc. Accordingly, the basestation can communicate a processing assignment with a neural networkformation configuration.

In some implementations, the base station 121 communicates multipleneural network formation configurations to the UE 110. For example, thebase station transmits a first message that directs the UE to use afirst neural network formation configuration for uplink encoding, and asecond message that directs the UE to use a second neural networkformation configuration for downlink decoding. In some scenarios, thebase station 121 communicates multiple neural network formationconfigurations, and the respective processing assignments, in a singlemessage. As yet another example, the base station communicates themultiple neural network formation configurations using different radioaccess technologies (RATs). The base station can, for instance, transmita first neural network formation configuration for downlinkcommunication processing to the UE 110 using a first RAT and/or carrier,and transmit a second neural network formation configuration for uplinkcommunication processing to the UE 110 using a second RAT and/orcarrier.

At 715, the UE 110 forms a first neural network based on the neuralnetwork formation configuration. For instance, the UE 110 accesses aneural network table using the index value(s) communicated by the basestation to obtain the neural network formation configuration and/or theneural network formation configuration elements. Alternately oradditionally, the UE 110 extracts neural network architecture and/orparameter configurations from the message. The UE 110 then forms theneural network using the neural network formation configuration, theextracted architecture and/or parameter configurations, etc. In someimplementations, the UE processes all communications using the firstneural network, while in other implementations, the UE processes selectcommunications using the first neural network based on a processingassignment.

At 720, the base station 121 communicates information based on theneural network formation configuration. For instance, with reference toFIG. 6, the base station 121 processes downlink communications using asecond neural network configured with complementary functionality to thefirst neural network. In other words, the second neural network uses asecond neural network formation configuration that is complementary tothe neural network formation configuration. In turn, at 725, the UE 110recovers the information using the first neural network.

A second example of signaling and control transactions for basestation-user equipment messaging regarding deep neural networks isillustrated by the signaling and control transaction diagram 800 of FIG.8. In some implementations, the signaling and control transactiondiagram 800 represents a continuation of the signaling and controltransaction diagram 700 of FIG. 7.

As illustrated, at 805, the base station 121 communicates with the UE110 based on a first neural network formation configuration. Similarly,at 810, the UE 110 communicates with the base station 121 based on thefirst neural network formation configuration. The base station 121, forinstance, communicates with the UE 110 by processing one or moredownlink communications using the DNNs 604 of FIG. 6, while the UE 110communicates with the base station 121 by processing downlinkcommunications received from the base station 121 using DNNs 608 of FIG.6.

In implementations, the DNNs 604 and the DNNs 608 are formed based onthe first neural network formation configuration as described. Toillustrate, the DNNs 604 and the DNNs 608 perform complementaryfunctionality of one another, where the first neural network formationconfiguration specifies the complementary functionality for each deepneural network (e.g., the base station forms a first neural networkusing the first neural network formation configuration, the UE forms asecond neural network that is complementary to the first neural networkby using a complementary neural network formation configuration to thefirst neural network formation configuration). The complementaryfunctionality performed by the deep neural networks allows each sideexchanging communications to stay synchronized (e.g., accurately recoverinformation). Thus, the first neural network formation configurationspecifies any combination of a base station-side neural networkformation configuration, a complementary user equipment-side neuralnetwork formation configuration, and/or a general neural networkformation configuration used by each device participating in thecommunication exchange.

As another example, the UE 110 processes one or more uplinkcommunications to the base station 121 using the DNNs 612 of FIG. 6,while the base station 121 processes the uplink communications receivedfrom the UE 110 using the deep neural network(s) 614 of FIG. 6. Similarto the downlink communications, some implementations form the DNNs 612and the deep neural network(s) 614 based on the first neural networkformation configuration (e.g., the UE forms a first neural network usingthe first neural network formation configuration, the base station formsa second neural network with a complementary neural network formationconfiguration to the first neural network formation configuration).Accordingly, the base station 121 and the UE 110 communicate with oneanother based on the first neural network formation configuration byforming deep neural networks based on the first neural network formationconfiguration, and processing communications with the deep neuralnetworks.

At 815, the base station generates base station metrics, such as metricsbased upon uplink communications received from the UE 110. Similarly, at820, the UE 110 generates UE metrics, such as metrics based upondownlink communications received from the base station 121. Any type ofmetric can be generated by the base station 121 and/or UE 110, such aspower measurements (e.g., RSS), error metrics, timing metrics, QoS,latency, and so forth.

At 825, the UE 110 communicates the metrics to the base station 121. Inimplementations, the UE 110 processes the metric communications using adeep neural network based on the first neural network formationconfiguration. Alternately or additionally, the UE 110 processes themetric communications using a neural network formed using a secondneural network formation configuration. Thus, as further described, UE110 maintains, in some implementations, multiple deep neural networks,where each deep neural network has a designated purpose and/orprocessing assignment (e.g., a first neural network for downlink controlchannel processing, a second neural network for downlink data channelprocessing, a third neural network for uplink control channelprocessing, a fourth neural network for uplink data channel processing).At times, the base station 121 communicates the multiple neural networkformation configurations, used to form the multiple deep neuralnetworks, to the UE 110.

At 830, the base station 121 identifies a second neural networkformation configuration based on the metrics at 830. In someimplementations, the base station 121 identifies the second neuralnetwork formation configuration based on the UE metrics, the basestation metrics, or any combination thereof. This includes identifyingany combination of architectural changes and/or parameter changes to theneural network formation configuration as further described, such as asmall change to the neural network formation configuration that involvesupdating coefficient parameters to address changes in returned metrics(e.g., SINR changes, Doppler feedback changes, power level changes, BLERchanges). Alternately or additionally, identifying the second neuralnetwork formation configuration includes a large change, such asreconfiguring node and/or layer connections, based on metrics such as achange in a power state (e.g., a transition from a radio resourceconnected state to idle state).

In some implementations, the base station 121 identifies a partialand/or delta neural network formation configuration as the second neuralnetwork formation configuration, where the partial and/or delta neuralnetwork formation configuration indicates changes to a full neuralnetwork formation configuration. A full neural network formationconfiguration, for example, includes an architectural configuration fora neural network and parameter configurations, while a partial and/ordelta neural network formation configuration specifies changes and/orupdates to the parameter configurations based on using the samearchitectural configuration indicated in the full neural networkformation configuration.

In some implementations, the base station identifies the second neuralnetwork formation configuration by identifying a neural networkformation configuration in a neural network table that improves the UE'sability, and/or the base station's ability, to recover data from thecommunications (e.g., improve an accuracy of the recovered information).To illustrate, the base station 121 identifies, by way of the basestation neural network manager 268 of FIG. 2, a neural network formationconfiguration that compensates for problems identified by the UE metricsand/or the base station metrics. As another example, the base station121 identifies a neural network formation configuration with one or moreinput characteristics that align with changing operating conditionsidentified by the UE metrics and/or the base station metrics.Alternately or additionally, the base station 121 identifies a neuralnetwork formation configuration that produces similar results but withless processing, such as for a scenario in which a UE moves to a lowerpower state.

At 840, the base station 121 directs the UE 110 to update the firstneural network with the second neural network formation configuration.The base station, for instance, generates an update message thatincludes an index value to the second neural network formationconfiguration in the neural network table 216 of FIG. 2. In someimplementations, the base station 121 indicates, in the message, a timeinstance that directs the UE 110 on when to apply the second neuralnetwork formation configuration. In other words, the time instancedirects the UE 110 to switch from processing communications using thefirst neural network formation configuration to processingcommunications using the second neural network formation configurationat the time specified in the time instance. In implementations, the basestation transmits updates to downlink neural networks using a firstcarrier or RAT, and transmits updates to uplink neural networks using asecond carrier or RAT.

At 845, the base station 121 updates a base station neural network basedon the second neural network formation configuration, such as the deepneural network formed based on the first neural network formationconfiguration and used to communicate with the UE 110 at 805 (e.g., adeep neural network for processing downlink communications, a deepneural network for processing uplink communications). Similarly, at 850,the UE 110 updates a user equipment neural network based on the secondneural network formation configuration, such as a deep neural networkthat performs complementary functionality to the base station neuralnetwork updated at 845. The UE, as one example, extracts the index valueand/or time value from the update message transmitted by the basestation at 840. The UE 110 obtains the second neural network formationconfiguration and modifies the user equipment neural network at the timespecified in the update message. Thus, the UE 110 uses the first neuralnetwork formation configuration for processing communications until thespecified time in the update message, at which point the UE 110 switchesto processing communications using the second neural network formationconfiguration.

In implementations, the base station 121 and/or the UE 110 iterativelyperform the signaling and control transactions described in thesignaling and control transaction diagram 800, signified in FIG. 8 withdashed lines. These iterations allow the base station 121 and/or the UE110 to dynamically modify communication processing chains based uponchanging operating conditions as further described.

A third example of signaling and control transactions for basestation-user equipment messaging regarding deep neural networks isillustrated by the signaling and control transaction diagram 900 ofFIGS. 9-1 and 9-2. As illustrated in FIG. 9-1, at 905, the core networkserver 302 determines a neural network formation configuration based ona variety of metrics and/or parameters, such as metrics from the UE 110,metrics from the base station 121, UE capabilities, etc. For example,the core network server receives any combination of metrics and/orparameters from the base station 121 and/or the UE 110, such as powerinformation, SINR information, CQI, CSI, Doppler feedback, QoS, latency,UE capabilities, a base station type (e.g., eNB, gNB or ng-eNB),protocol versions, error metrics, UE capabilities, BS capabilities,power mode, and so forth. The core network server 302 then determinesthe neural network formation configuration based on the metrics,parameters, etc.

In some implementations, the core network server 302 determines multipledifferent neural network formation configurations, each of which isspecific to a respective base station and/or respective UE. Alternatelyor additionally, the core network server 302 determines a neural networkformation configuration used by multiple base stations and/or UEs. Attimes, the core network server 302 determines a default neural networkformation configuration used by base stations and/or UEs to initiallyconnect with one another. As further described, a default neural networkformation configuration corresponds to a general neural networkformation configuration that configures the deep neural network toprocess a variety of input data and/or channel conditions with anaccuracy within a threshold range or value. A dedicated neural networkformation configuration, however, corresponds to a deep neural networkthat is tuned to a particular type of input data and/or particularchannel conditions. In determining the neural network formationconfiguration, some implementations of the core network server determinecomplementary neural network formation configurations (e.g., a basestation-side neural network formation configuration that iscomplementary to a user equipment-side neural network formationconfiguration).

At 910, the core network server 302 communicates the neural networkformation configuration to the base station 121. For instance, the corenetwork server 302 communicates an index value to the base station 121over the core network interface 318 of FIG. 3, where the index valuemaps to an entry in the neural network table 272 of FIG. 2. Alternatelyor additionally, the core network server 302 communicates variousparameter configurations, such as coefficients, weights, layerconnection values, etc.

At 915, the base station 121 forwards the neural network formationconfiguration to the UE 110. The base station, for instance, wirelesslytransmits the index value to the UE, wirelessly transmits the parameterconfigurations to the UE, and so forth. In communicating the neuralnetwork formation configuration, the core network server 302 and/or thebase station 121 sometimes indicates a processing assignment for theneural network formation configuration (e.g., downlink data channelprocessing, uplink control channel processing, decoding processing,uplink encoding processing, uplink modulating processing, downlinkdemodulating processing).

In some implementations, the base station 121 adds modifications theneural network formation configuration before forwarding to the UE 110.For example, the base station 121 can have access to more updatedinformation than the core network server, such as by through UEcapabilities received from the UE 110. The base station 121, at times,adapts the neural network formation configuration based upon the updatedinformation (e.g., UE capabilities particular to the UE 110), such as byremoving layers and/or nodes to reduce a corresponding complexity of thedeep neural network at the UE 110 based available processingcapabilities, batter power, available radios, etc. at the UE. As anotherexample, the base station 121 adds convolutional layers to the neuralnetwork formation configuration based on the updated information.Afterwards, the base station 121 forwards the modified neural networkformation configuration to the UE 110, in lieu of the neural networkformation configuration received from the core network server 302.

At 920, the base station 121 forms a first deep neural network using theneural network formation configuration, such as by identifying a basestation-side neural network formation configuration and forming thefirst deep neural network with the base station-side neural networkformation configuration. To illustrate, the base station 121 obtains theneural network formation configuration by using an index value to accessneural network table 272 of FIG. 2. Similarly, at 925, the UE 110 formsa second deep neural network using the neural network formationconfiguration, such as by identifying a complementary and/or userequipment-side neural network formation configuration and forming thesecond neural network with the complementary and/or user equipment-sideneural network formation configuration. In some implementations, the UE110 obtains the complementary and/or user equipment-side neural networkformation configuration by using an index value to access the neuralnetwork table 216 of FIG. 2. Accordingly, the first deep neural networkand the second deep neural network are synchronized neural networksbased on the neural network formation configuration determined andcommunicated by the core network server at 905 and at 910.

In FIG. 9-2, at 930, the base station 121 communicates with the UE 110using the first deep neural network, such as by generating and/orprocessing downlink communications to the UE 110, by receiving and/orprocessing uplink communications from the UE 110, etc. Similarly, at935, the UE 110 communicates with the base station 121 using the seconddeep neural network at 935. In other words, the UE 110 communicates withthe base station 121 using a complementary deep neural network (e.g.,the second deep neural network) that is based on the neural networkformation configuration as further described.

At 940, the base station generates base station metrics, where the basestation 121 generates the metrics based on the communicating at 930and/or at 935. Thus, the base station metrics can be based on uplinkcommunications received from the UE 110. For example, the base station121 generates uplink received power, uplink SINR, uplink packet errors,uplink throughput, timing measurements, and so forth. In someimplementations, the base station includes base station capabilities (BScapabilities), such as processing power (e.g., macro base station,small-cell base station), power state, etc., in the base stationmetrics.

Similarly, at 945, the UE 110 generates UE metrics (e.g., powerinformation, SINR information, CQI, CSI, Doppler feedback, QoS, latency)based on downlink communications from the base station 121 andcommunicates the UE metrics to the base station 121 at 950.

At 955, the base station 121 forwards the metrics to the core networkserver 302, such as through core network interface 318. This includesany combination of the base station metrics and the UE metrics generatedat 940 and/or at 945. Afterwards, the core network server 302 determinesupdates to the neural network formation configuration at 960. This caninclude any combination of architectural structure changes (e.g.,reconfiguring node connections, reconfiguring active layers/inactivelayers), changing applied processing parameters (e.g., coefficients,kernels), and so forth. Thus, the core network server 302, at times,identifies small changes and/or large changes, such as those describedwith reference to the base station 121 at 830 of FIG. 8. By receivingmetrics generated from UE 110, by way of base station 121, the corenetwork server 302 receives feedback about the communication channelbetween the base station and the UE and/or an indication of how well theneural network processes communications transmitted over thecommunication channel. The core network server 302 analyzes the feedbackto identify adjustments to the neural network formation configurationthat, when applied to the neural network, improve the accuracy of howthe deep neural network processes communications (e.g., more accuraterecovery of data, less erroneous data) and/or how efficiently the neuralnetwork processes the communications (e.g. selecting configurations thatreduce processing time of the deep neural network).

In implementations, the core network server 302, base station 121 and/orthe UE 110 iteratively perform the signaling and control transactionsdescribed in the signaling and control transaction diagram 900,signified in FIGS. 9-1 and 9-2 with a dashed line that returns from 960to 910. These iterations allow the core network server 302, the basestation 121 and/or the UE 110 to dynamically modify communicationprocessing chains based upon changing operating conditions as furtherdescribed.

In some implementations, core network server 302 receives feedback frommultiple UEs and/or base stations in the wireless communication system.This provides the core network server with a larger view of how well thewireless communication system performs, what devices communicate overthe wireless communication system, how well the devices communicate, andso forth. In various implementations, the core network server 302determines updates to the neural network formation configuration basedon optimizing the communications of the multiple devices and/or anoverall system performance, rather than optimizing the communications ofa particular device.

Having described signal and control transactions that can be used toconfigure neural networks for processing communications, consider nowsome example methods that are in accordance with one or moreimplementations.

Example Methods

Example methods 1000 and 1100 are described with reference to FIG. 10and FIG. 11 in accordance with one or more aspects of base station-userequipment messaging regarding deep neural networks. The order in whichthe method blocks are described are not intended to be construed as alimitation, and any number of the described method blocks can be skippedor combined in any order to implement a method or an alternate method.Generally, any of the components, modules, methods, and operationsdescribed herein can be implemented using software, firmware, hardware(e.g., fixed logic circuitry), manual processing, or any combinationthereof. Some operations of the example methods may be described in thegeneral context of executable instructions stored on computer-readablestorage memory that is local and/or remote to a computer processingsystem, and implementations can include software applications, programs,functions, and the like. Alternatively, or additionally, any of thefunctionality described herein can be performed, at least in part, byone or more hardware logic components, such as, and without limitation,Field-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip systems (SoCs), Complex Programmable Logic Devices(CPLDs), and the like.

FIG. 10 illustrates an example method 1000 for configuring a neuralnetwork for processing communications exchanged over a wirelesscommunication system. In some implementations, operations of method 1000are performed by a network entity, such as the base station 121 or thecore network server 302.

At 1005, a network entity determines a neural network formationconfiguration for a deep neural network for processing communicationstransmitted over the wireless communication system. The network entity(e.g., base station 121, core network server 302) for example,determines the neural network formation configuration based, at least inpart, on metrics, feedback, and/or other types of information from theuser equipment (e.g., UE 110). To illustrate, the base station 121receives a message from the UE 110 that indicates one or morecapabilities of the UE 110. The base station 121 then determines theneural network formation configuration based, at least in part, on thecapabilities received from the UE 110. Alternately or additionally, basestation 121 forwards the UE capabilities to the core network server 302,and the core network server 302 determines the neural network formationconfiguration based on the capabilities of the UE 110. As anotherexample, the base station 121 determines the neural network formationconfiguration based on scheduling MU-MIMO downlink and/or uplinktransmissions in the wireless communication system. In determining theneural network formation configuration, the network selects anycombination of neural network formation configuration elements for userequipment-side deep neural networks, base station-side deep neuralnetworks, and/or a deep neural network that corresponds to both the userequipment-side deep neural networks and the base station-side deepneural networks.

In determining the neural network formation configuration, the networkentity (e.g., base station 121, the core network server 302) sometimesselects a default neural network formation configuration. Alternately oradditionally, the network entity analyzes multiple neural networkformation configurations, and selects a neural network formationconfiguration, from the multiple, that aligns with current channelconditions, capabilities of a particular UE, etc.

At 1010, the network entity generates a message that includes anindication of the neural network formation configuration for the deepneural network. The network entity (e.g., base station 121, core networkserver 302), for instance, generates a message that includes indexvalue(s) that map(s) to one or more entries of a neural network table(e.g., neural network table 216, neural network table 272, neuralnetwork table 316). In some implementations, the network entity includesan indication of a processing assignment for the deep neural network,where the processing assignment specifies a processing chain functionfor the deep neural network formed with the neural network formationconfiguration. Alternately or additionally, the network entity specifiesa time instance in the message that indicates a time and/or location tostart processing communications with the deep neural network.

At 1015, the network entity transmits the message to a user equipment todirect the user equipment to form the deep neural network using theneural network formation configuration and to process the communicationstransmitted over the wireless communication system using the deep neuralnetwork. The network entity (e.g., core network server 302), forexample, transmits the message to the user equipment (e.g., UE 110) bycommunicating the message to the base station 121, which transmits themessage to the user equipment. Alternately or additionally, the networkentity (e.g., base station 121) transmits the message to the userequipment (e.g., UE 110). In implementations the deep neural networkcorresponds to a user equipment-side deep neural network.

In some implementations, the network entity (e.g., base station 121)transmits the message using a particular RAT and/or carrier based upon aprocessing assignment of the deep neural network. For instance, the basestation 121 transmits a first neural network formation configurationwith a first processing assignment (e.g., downlink data channelprocessing) using a first RAT and/or carrier, and a second neuralnetwork formation configuration with a second processing assignment(e.g., downlink control channel processing) using a second RAT and/orcarrier.

In some implementations, at 1020, the network entity analyzes feedbackreceived from the user equipment. The network entity (e.g., base station121), as one example, receives metrics from the user equipment (e.g., UE110), and analyzes the metrics to determine whether to update and/orreconfigure the neural network with a different neural network formationconfiguration. Alternately or additionally, a base station forwards thefeedback to the network entity (e.g., core network server 302), and thenetwork entity analyzes the feedback to determine whether to updateand/or reconfigure the neural network with a different neural networkformation configuration.

At 1025, the network entity updates the deep neural network based on thefeedback. For example, the network entity (e.g., base station 121, corenetwork server 302) analyzes multiple neural network formationconfigurations included in a neural network table (e.g., neural networktable 216, neural network table 272, neural network table 316), andselects a second neural network formation configuration that aligns withnew channel conditions indicated by the feedback. The network entitygenerates a second message that includes an indication of the secondneural network formation configuration, and transmits the second messageto the user equipment. This includes configuring and transmitting thesecond message as described at 1010 and/or at 1015. In implementations,the network entity iteratively performs various operations of the method1000, indicated here with a dashed line that returns from 1025 to 1010.

FIG. 11 illustrates an example method 1100 for forming a neural networkbased on a neural network formation configuration. In some aspects,operations of method 1100 are implemented by a UE, such as UE 110.

At 1105, a user equipment receives a message that indicates a neuralnetwork formation configuration for a deep neural network for processingcommunications transmitted over a wireless communication system. Theuser equipment (e.g., UE 110), for instance, receives a message from abase station (e.g., the base station 121) that indicates the neuralnetwork formation configuration and/or a processing assignment for thedeep neural network formed using the neural network formationconfiguration. In some implementations, the user equipment receivesmultiple messages, each of which pertains to a different neural networkformation configuration (e.g., a first message that indicates a firstneural network formation configuration for a first deep neural networkfor processing downlink communications, a second message that indicatesa second neural network formation configuration for a second deep neuralnetwork for processing uplink communications). In implementations, theuser equipment receives the message as a broadcast message from the basestation, or as a UE-dedicated message.

At 1110, the user equipment forms the deep neural network using theneural network formation configuration indicated in the message. Toillustrate, the user equipment (e.g., UE 110) extracts parameterconfigurations from the message, such as coefficients, weights, layerconnections, etc., and forms the deep neural network using the extractedparameter configurations. As another example, the user equipment (e.g.,UE 110) extracts index value(s) from the message and obtains theparameter configurations from a neural network table as furtherdescribed. Alternately or additionally, the user equipment extracts atime instance from the message, where the time instance indicates a timeand/or location to start processing communications using the deep neuralnetwork formed with the neural network formation configuration.

At 1115, the user equipment receives communications from a base station.For example, the user equipment (e.g., UE 110) receives downlink datachannel communications from a base station (e.g., base station 121),downlink control channel communications from the base station, etc. At1120, the user equipment processes the communications using the deepneural network to extract information transmitted in the communications.The user equipment (e.g., UE 110), as one example, processes thecommunications using the deep neural network based on a processingassignment included in the message, such as by demodulating and/ordecoding downlink communications from the base station (e.g., basestation 121) using the deep neural network, encoding and/or modulatinguplink communications to the base station, and so forth.

In some implementations, the user equipment optionally transmitsfeedback based on the communications at 1125. For instance, the userequipment (e.g., UE 110) generates metrics based on the communications(e.g., error metrics, SINR information, CQI, CSI, Doppler feedback) andtransmits the metrics as feedback to the base station (e.g., basestation 121).

Having described some example methods that can be used to implementaspects of base station-user equipment messaging using a deep neuralnetwork, consider now a discussion of generating and communicatingneural network formation configurations that is in accordance with oneor more implementations.

Generating and Communicating Neural Network Formation Configurations

In supervised learning, machine-learning modules process labeledtraining data to generate an output. The machine-learning modulesreceive feedback on an accuracy of the generated output and modifyprocessing parameters to improve the accuracy of the output. FIG. 12illustrates an example 1200 that describes aspects of generatingmultiple NN formation configurations. At times, various aspects of theexample 1200 are implemented by any combination of training module 270,base station neural network manager 268, core network neural networkmanager 312, and/or training module 314 of FIG. 2 and FIG. 3.

The upper portion of FIG. 12 includes machine-learning module 400 ofFIG. 4. In implementations, a neural network manager determines togenerate different NN formation configurations. To illustrate, considera scenario in which the base station neural network manager 268determines to generate a NN formation configuration by selecting acombination of NN formation configuration elements from a neural networktable, where the NN formation configuration corresponds to a UE decodingand/or demodulating downlink communications. In other words, the NNformation configuration (by way of the combination of NN formationconfiguration elements) forms a DNN that processes downlinkcommunications received by a UE. Oftentimes, however, transmissionchannel conditions vary which, in turn, affects the characteristics ofthe downlink communications. For instance, a first transmission channeldistorts the downlink communications by introducing frequency offsets, asecond transmission channel distorts the downlink communications byintroducing Doppler effects, a third transmission channel distorts thedownlink communications by introducing multipath channel effects, and soforth. To accurately process the downlink communications (e.g., reducebit errors), various implementations select multiple NN formationconfigurations, where each NN formation configuration (and associatedcombination of NN formation configuration elements) corresponds to arespective input condition, such as a first transmission channel, asecond transmission channel, etc.

Training data 1202 represents an example input to the machine-learningmodule 400. In FIG. 12, the training data represents data correspondingto a downlink communication. Training data 1202, for instance, caninclude digital samples of a downlink communications signal, recoveredsymbols, recovered frame data, etc. In some implementations, thetraining module generates the training data mathematically or accesses afile that stores the training data. Other times, the training moduleobtains real-world communications data. Thus, the training module cantrain the machine-learning module using mathematically generated data,static data, and/or real-world data. Some implementations generate inputcharacteristics 1204 that describe various qualities of the trainingdata, such as transmission channel metrics, UE capabilities, UEvelocity, and so forth.

Machine-learning module 400 analyzes the training data, and generates anoutput 1206, represented here as binary data. Some implementationsiteratively train the machine-learning module 400 using the same set oftraining data and/or additional training data that has the same inputcharacteristics to improve the accuracy of the machine-learning module.During training, the machine-learning module modifies some or all of thearchitecture and/or parameter configurations of a neural networkincluded in the machine-learning module, such as node connections,coefficients, kernel sizes, etc. At some point in the training, thetraining module determines to extract the architecture and/or parameterconfigurations 1208 of the neural network (e.g., pooling parameter(s),kernel parameter(s), layer parameter(s), weights), such as when thetraining module determines that the accuracy meets or exceeds a desiredthreshold, the training process meets or exceeds an iteration number,and so forth. The training module then extracts the architecture and/orparameter configurations from the machine-learning module to use as a NNformation configuration and/or NN formation configuration element(s).The architecture and/or parameter configurations can include anycombination of fixed architecture and/or parameter configurations,and/or variable architectures and/or parameter configurations.

The lower portion of FIG. 12 includes neural network table 1212 thatrepresents a collection of NN formation configuration elements, such asneural network table 216, neural network table 272, and/or neuralnetwork table 316 of FIG. 2 and FIG. 3. The neural network table 1212stores various combinations of architecture configurations, parameterconfigurations, and input characteristics, but alternate implementationsexclude the input characteristics from the table. Variousimplementations update and/or maintain the NN formation configurationelements and/or the input characteristics as the machine-learning modulelearns additional information. For example, at index 1214, the neuralnetwork manager and/or the training module updates neural network table1212 to include architecture and/or parameter configurations 1208generated by the machine-learning module 400 while analyzing thetraining data 1202.

The neural network manager and/or the training module alternately oradditionally adds the input characteristics 1204 to the neural networktable and links the input characteristics to the architecture and/orparameter configurations 1208. This allows the input characteristics tobe obtained at a same time as the architecture and/or parameterconfigurations, such as through using an index value that referencesinto the neural network table (e.g., references NN formationconfigurations, references NN formation configuration elements). In someimplementations, the neural network manager selects a NN formationconfiguration by matching the input characteristics to a currentoperating environment, such as by matching the input characteristics tocurrent channel conditions, UE capabilities, UE characteristics (e.g.,velocity, location, etc.) and so forth.

Having described generating and communicating neural network formationconfigurations, consider now a discussion of signal and controltransactions over a wireless communication system that can be used tocommunicate neural network formation configurations that is inaccordance with one or more implementations

Signaling and Control Transactions to Communicate Neural NetworkFormation Configurations

FIGS. 13-15 illustrate example signaling and control transactiondiagrams between a base station, a user equipment, and/or a core networkserver in accordance with one or more aspects of communicating neuralnetwork formation configurations, such as communicating a NN formationconfiguration. In implementations, the signaling and controltransactions may be performed by the base station 121 and the UE 110 ofFIG. 1, or the core network server 302 of FIG. 3, using elements ofFIGS. 1-12.

A first example of signaling and control transactions for communicatingneural network formation configurations is illustrated by the signalingand control transaction control diagram 1300 of FIG. 13. Inimplementations, portions or all of the signaling and controltransactions described with reference to the signaling and controltransaction control diagram correspond to signaling and controltransactions described with reference to FIG. 7 and/or FIG. 8.

As illustrated, at 1305 the base station 121 maintains a neural networktable. For example, a base station neural network manager and/or thetraining module of the base station 121 (e.g., base station neuralnetwork manager 268, training module 270) generate and/or maintain aneural network table (e.g., neural network table 272) using anycombination of mathematically generated training data, data extractedfrom real-world communications, files, etc. In various implementations,the base station 121 maintains multiple neural network tables, whereeach neural network table includes multiple neural network formationconfigurations and/or neural network formation configuration elementsfor a designated purpose, such as a first neural network tabledesignated for data channel communications, a second neural networktable designated for control channel communications, and so forth.

At 1310, the base station 121 transmits the neural network table to theUE 110. As one example, the base station transmits the neural networktable using layer 3 messaging (e.g., Radio Resource Control (RRC)messages). In transmitting the neural network table, the base stationtransmits any combination of architecture and/or parameterconfigurations that can be used to form a deep neural network, examplesof which are provided in this disclosure. Alternately or additionallythe base station transmits an indication with the neural network tablethat designates a processing assignment for the neural network table.Accordingly, the base station transmits multiple neural network tablesto the UE, with a respective processing assignment designated for eachneural network table. In some implementations, the base station 121broadcasts the neural network table(s) to a group of UEs. Other times,the base station 121 transmits a UE-dedicated neural network table tothe UE 110.

At 1315, the base station 121 identifies a neural network formationconfiguration to use in processing communications. For example, the basestation determines a neural network formation configuration to use inprocessing the communications by selecting a combination of neuralnetwork formation architecture elements, such as that described at 705of FIG. 7, by analyzing any combination of information, such as achannel type being processed by a deep neural network (e.g., downlink,uplink, data, control, etc.), transmission medium properties (e.g.,power measurements, signal-to-interference-plus-noise ratio (SINR)measurements, channel quality indicator (CQI) measurements), encodingschemes, UE capabilities, BS capabilities, and so forth. Thus, in someimplementations, the base station 121 identifies the neural networkformation configuration base on receiving various metrics and/orparameters, such as that described at 815, 820, and 830 of FIG. 8. Insome implementations, the base station 121 identifies a default neuralnetwork formation configuration to use as the neural network formationconfiguration. Thus, identifying the neural network formationconfiguration can include identifying default neural network formationconfiguration and/or updates to a neural network formationconfiguration.

In identifying the neural network formation configuration, the basestation 121 ascertains a neural network formation configuration in theneural network table that corresponds to the determined neural networkformation configuration. In other words, the base station 121 identifiesa neural network formation configuration and/or neural network formationconfiguration elements in neural network table 272 and/or neural networktable 216 of FIG. 2 that align with the determined neural networkformation configuration, such as by correlating and or matching inputcharacteristics. In identifying the neural network formationconfiguration and/or neural network formation configuration elements inthe neural network table, the base station identifies index value(s) ofthe neural network formation configuration and/or neural networkformation configuration elements.

At 1320, the base station 121 transmits an indication that directs theUE 110 to form a deep neural network using a neural network formationconfiguration from the neural network table. For example, similar tothat described at 710 of FIG. 7 and/or at 840 of FIG. 8, the basestation 121 communicates the index value(s) to the UE 110, and directsthe UE 110 to form the deep neural network using the neural networkformation configuration indicated by the index value(s). The basestation can transmit the indication to the UE in any suitable manner. Asone example, the base station transmits index value(s) that correspondsto the neural network formation configuration using a layer 2 message(e.g., a Radio Link Control (RLC) message, Medium Access Control (MAC)control element(s)). In some implementations, the base station comparesa current operating condition (e.g., channel conditions, UEcapabilities, BS capabilities, metrics) to input characteristics storedwithin the neural network table and identifies stored inputcharacteristics aligned with the current operating condition. In turn,the base station obtains the index value of stored input characteristicswhich, in turn, provides the index value of the neural network formationconfiguration and/or neural network formation configuration elements.The base station 121 then transmits the index value(s) as theindication. At times, the base station 121 includes a processingassignment to indicate a position in a processing chain to apply thedeep neural network. In some implementations, the base station transmitsthe index value(s) and/or processing assignment using a downlink controlchannel.

At times, the base station transmits rule(s) to the UE specifyingoperating parameters related to applying the neural network formationconfiguration. In one example, the rules include a time instance thatindicates when to process communications with the deep neural networkformed using the neural network formation configuration. Alternately oradditionally, the rules specify a time threshold value that directs theUE to use a default neural network formation configuration instead ofthe specified neural network formation configuration when a data channeland control channel are within the time threshold value. Additionally oralternatively, a rule may direct the user equipment to use the sameneural network formation configuration for data channel communicationsand control channel communications when a data channel and controlchannel are within the time threshold value. To illustrate, consider anexample in which the UE processes data channel communications using afirst deep neural network (formed using a first neural network formationconfiguration), and control channel communications using a second deepneural network (formed using a second neural network formationconfiguration). If the data channel communications and control channelcommunications fall within the time threshold value specified by thetime instance, the UE processes both channels using a default deepneural network (formed with the default neural network formationconfiguration) and/or the same deep neural network, since there may notbe enough time to switch between the first deep neural network and thesecond deep neural network.

At times, the base station specifies a default neural network formationconfiguration to UE(s) using a downlink control channel to communicatethe default neural network formation configuration, where the defaultneural network formation configuration forms a deep neural network thatprocesses a variety of input data. In some implementations, the defaultneural network formation configuration forms a deep neural network thatprocesses the variety of input data with an accuracy within a thresholdrange. The default neural network formation configuration can include ageneric neural network formation configuration.

To illustrate, some implementations generate or select neural networkformation configurations for specific operating conditions, such as afirst neural network formation configuration specific to UE downlinkcontrol channel processing (e.g., demodulating and/or deciding) with acurrent operating condition “X”, a second neural network formationconfiguration specific to UE downlink control channel processing with acurrent operating condition “Y”, and so forth. For example, a firstneural network formation configuration can correlate to a currentoperating environment in which a detected interference level is high, asecond neural network formation configuration can correlate to a currentoperating environment in which a detected interference level is low, athird neural network formation configuration can correlate to a currentoperating environment in which a connected UE appears stationary, afourth neural network formation configuration can correlate to a currentoperating environment in which the connected UE appears to be moving andwith a particular velocity, and so forth.

Forming a deep neural network using a neural network formationconfiguration for specific operating conditions improves (relative toforming the deep neural network with other neural network formationconfigurations) an accuracy of the output generated by the deep neuralnetwork when processing input data corresponding to the specificoperating conditions. However, this introduces a tradeoff insofar as thedeep neural network formed with the neural network formationconfiguration for specific operating conditions generates output withless accuracy when processing input associated with other operatingconditions. Conversely, a default neural network formation configurationcorresponds to a neural network formation configuration that processes awider variety of input, such as a variety of input that spans moreoperating conditions. In other words, a deep neural network configuredwith a default neural network formation configuration processes a largervariety of communications relative to neural network formationconfigurations directed to specific operating conditions.

At 1325, the UE 110 forms the deep neural network using the neuralnetwork formation configuration. The UE, as one example, extracts theindex value(s) transmitted by the base station 121, and obtains theneural network formation configuration and/or neural network formationconfiguration elements by accessing the neural network table using theindex value(s). Alternately or additionally, the UE 110 extracts theprocessing assignment, and forms the deep neural network in theprocessing chain as specified by the processing assignment.

At 1330, the base station 121 transmits communications to the UE 110,such as downlink data channel communications. At 1335, the UE 110processes the communications using the deep neural network. Forinstance, the UE 110 processes the downlink data channel communicationsusing the deep neural network to recover the data. As another example,processing the communications includes processing a reply to thecommunications, where the UE 110 processes, using the deep neuralnetwork, uplink communications in reply to the downlink communications.

A second example of signaling and control transactions for communicatingneural network formation configurations is illustrated by the signalingand control transaction diagram 1400 of FIG. 14. As illustrated, at 1405the core network server 302 maintains a neural network table. The corenetwork neural network manager 312 and/or the training module 314 of thecore network server 302, for instance, generate and/or maintain theneural network table 316 using any combination of mathematicallygenerated training data, data extracted from real-world communications,files, etc. In various implementations, the core neural network server302 maintains multiple neural network tables, where each neural networktable includes multiple neural network formation configuration elementsfor a designated processing assignment (e.g., a first neural networktable designated for data channel communications, a second neuralnetwork table designated for control channel communications).

At 1410, the core network server 302 communicates the neural networktable to the base station 121, such as by using the core networkinterface 318 of FIG. 3. In some implementations, the core networkserver communicates multiple neural network tables to the base station.At 1415, the base station 121 transmits the neural network table to theUE 110, such as by transmitting the neural network table using layer 3messaging, by transmitting the neural network table using a downlinkcontrol channel, by broadcasting the neural network table, bytransmitting the neural network table using a UE-dedicated message, andso forth.

At 1420, the core network server 302 selects a neural network formationconfiguration. As one example, the core network server 302 compares acurrent operating condition to input characteristics stored within theneural network table and identifies stored input characteristics alignedwith the current operating condition (e.g., channel conditions, UEcapabilities, BS capabilities, metrics). The core network server thenobtains the index value(s) of the aligned input characteristics which,in turn, provides the index value(s) of the neural network formationconfiguration and/or neural network formation configuration elements.The core network server 302 then communicates the selected neuralnetwork formation configuration to the base station at 1425, such as bycommunicating the index value(s) using core network interface 318. Insome implementations, the core network server communicates a processingassignment with the neural network formation configuration.

At 1430, the base station 121 forwards the neural network formationconfiguration to the UE 110. As an example, the base station 121transmits the index value(s) to the UE 110, such as through layer 2messaging (e.g., an RLC message, MAC control element(s)), to direct theUE to form the deep neural network using the neural network formationconfiguration, such as that described at 1320 of FIG. 13. Inimplementations, the base station 121 additionally forms a complementarydeep neural network based on the neural network formation configuration(not shown here), such as that described in FIG. 6, at 720 of FIG. 7, at845 of FIG. 8, and/or at 920 of FIG. 9-1. At times, the base stationcommunicates the processing assignment received from the core networkserver with the index value. In response to receiving the neural networkformation configuration, the UE 110 forms the deep neural network at1435, and processes communications received from the base station 121using the deep neural network at 1440, such as that described at 725 ofFIG. 7, at 810 of FIG. 8, at 935 of FIG. 9, and/or 1335 of FIG. 13.

In some implementations, a UE derives a first neural network formationconfiguration from a second neural network formation configuration whensimilarities are present in a wireless communication system. Toillustrate, consider an example of quasi-correspondent channels.Quasi-correspondent channels are channels within a wirelesscommunication system that have shared or the same properties, such as asame delay spread, a same Doppler spread, a same spatial signature, asame spatial beam, a same spatial direction, same data rate, and soforth. Alternately or additionally, quasi-correspondent channels havecorrelated physical properties within a threshold range or value. Invarious implementations, a UE derives a first neural network formationconfiguration from a second neural network formation configuration inresponse to identifying these similarities.

To illustrate, consider now a third example of signaling and controltransactions for communicating neural network formation configurations,illustrated in FIG. 15 by the signaling and control transaction diagram1500. In the third example, a UE derives a neural network formationconfiguration based on identified similarities. While the signaling andcontrol transaction diagram 1500 illustrates the base station 121 andthe UE 110 of FIG. 1, alternate implementations include a core networkserver, such as core network server 302 of FIG. 3 performing some or allof the functionality performed by the base station 121.

At 1505, the base station 121 identifies two or more channels that arequasi-correspondent channels. The base station 121 or core networkserver 302 (using base station neural network manager 268 of FIG. 2 orcore network neural network manager 312 of FIG. 3), for example,compares various beam properties of two or more channels, such asdirection, intensity, divergence, profile, quality, etc. The basestation 121 determines that the two or more channels arequasi-correspondent when an arbitrary number of properties match and/orcorrelate to one another within a predefined threshold.

At 1510, the base station 121 transmits an indication of thequasi-correspondent channels to the UE 110, such as by transmitting theindication of quasi-correspondent channels using layer 2 messaging,layer 3 messaging, and/or layer 1 signaling. The indication denotes anyarbitrary number of channels as being quasi-correspondent with oneanother, where the channels can be physical channels and/or logicalchannels. As one example, the indication denotes that a downlink controlchannel is quasi-correspondent with a downlink data channel when thechannels have similar data rates. As another example, the indicationdenotes that two physical channels at different carrier frequencies arequasi-correspondent based on the physical channels having a similarspatial beam.

At 1515, the base station 121 transmits an indication of a neuralnetwork formation configuration for one of the quasi-correspondentchannels. For example, the base station transmits index value(s) andprocessing assignment that designates the formed deep neural network forprocessing a first channel, such as a downlink control channel.

At 1520, the UE 110 forms a first deep neural network using the neuralnetwork formation configuration for the first channel (e.g., thedownlink control channel). At 1525, the UE 110 identifies the firstchannel as one of the quasi-correspondent channels. The UE, forinstance, compares the first channel to the quasi-correspondent channelsidentified by, and received from, the base station 121. In response toidentifying the first channel as being one of the quasi-correspondentchannels, the UE determines to apply the neural network formationconfiguration to the other channels that are quasi-correspondent to thefirst channel. Accordingly, at 1530, the UE 110 forms deep neuralnetworks for the other quasi-correspondent channels using the neuralnetwork formation configuration. By identifying channels asquasi-correspondent channels, the neural network formation configurationneed only be changed for one of the quasi-correspondent channels andthis will result in the neural network formation configuration beingchanged for all the quasi-correspondent channels without the need tochange individually the neural network formation configuration for eachof the other quasi-correspondent channels.

Having described signal and control transactions that can be used tocommunicate neural network formation configurations, consider now anexample environment that can be used to communicate neural networkformation configurations by using a set of candidate neural networkformation configurations that is in accordance with one or moreimplementations.

Communicating a Set of Candidate Neural Network Formation Configurations

In some implementations, a network entity communicates a set ofcandidate NN formation configuration elements to a UE, and the UEselects one NN formation configuration from the set of candidate NNformation configuration elements. To illustrate, consider FIGS. 16-1 and16-2 that illustrate an example environment 1600 for communicatingneural network formation configurations using a set of candidate neuralnetwork formation configuration elements. FIGS. 16-1 and 16-2 illustratethe example environment 1600 at different points in time, labeled asenvironment 1600-1, environment 1600-2, environment 1600-3, andenvironment 1600-4, respectively. Thus, the environment 1600-1, theenvironment 1600-2, the environment 1600-3, and the environment 1600-4collectively represent the environment 1600 over a progression ofevents.

In the environment 1600-1, the base station neural network manager 268of the base station 121 selects at least one set of candidate NNformation configuration elements from the neural network table 1212 ofFIG. 12. For example, the base station neural network manager 268receives various metrics, parameters, and/or other types of feedback,such as that described at 815 and at 825 of FIG. 8. The base stationneural network manager 268 then analyzes the neural network table 1212to identify NN formation configuration elements that align with thefeedback, correct for problems identified by the feedback, and so forth.

In implementations, the base station neural network manager identifiesmultiple sets of NN formation configuration elements, such as byidentifying NN formation configurations with input characteristics thatfall within a threshold range. In the environment 1600-1, the basestation neural network manager 268 identifies and selects three sets ofNN formation configuration elements: NN formation configuration elementset 1602, NN formation configuration element set 1604, and NN formationconfiguration element set 1606. In some implementations, each NNformation configuration element set corresponds to a respective NNformation configuration.

In environment 1600-1, the NN formation configuration element set 1602includes index values 1608, 1610, and 1612, the NN formationconfiguration element set 1604 includes index values 1614, 1616, and1618, and the NN formation configuration element set 1606 includes indexvalues 1620, 1622, and 1624. While environment 1600-1 illustrates thebase station neural network manager 268 of the base station 121selecting the sets of candidate NN formation configuration elements,alternate implementations select the set of candidate NN formationconfiguration elements using the core network neural network manager 312of the core network server 302 or both. Alternately or additionally, thebase station neural network manager and/or the core network neuralnetwork manager select a single index value which maps to a grouping ofNN formation configuration elements.

In the environment 1600-2, the base station 121 transmits, to the UE110, an indication 1608 that references each set of candidate NNformation configuration elements (e.g., NN formation configurationelement set 1602, NN formation configuration element set 1604, NNformation configuration element set 1606). For example, the indicationincludes the index values 1602, 1604, 1606, 1608, 1610, 1612, 1614,1616, and 1618 to communicate the three sets of candidate NN formationconfiguration elements to the UE 110. Alternately or additionally, theindication includes directions to select a NN formation configurationfrom a set of candidate NN formation configurations. While theenvironment 1600-1 and the environment 1600-2 illustrate sets of NNformation configuration elements corresponding to a candidate NNformation configuration, alternate implementations select a single indexfor each candidate NN formation configuration that maps to a grouping ofelements as further described.

In the environment 1600-3 of FIG. 16-2, the UE neural network manager218 of the UE 110 analyzes the sets of candidate NN formationconfiguration elements to determine which of the sets to use forprocessing particular communications (e.g., for processing transmissioncommunications, for processing received communications). In one example,the UE neural network manager forms a first candidate deep neuralnetwork 1628 (candidate DNN 1628) based on the first NN formationconfiguration element set 1602 (e.g., index values 1608, 1610, 1612), asecond candidate deep neural network 1630 (candidate DNN 1630) based onthe second NN formation configuration element set 1604 (e.g., indexvalues 1608, 1610, 1612), a third candidate deep neural network 1632(candidate DNN 1632) based on the third NN formation configurationelement set 1606 (e.g., index values 1614, 1616, 1618), and so forth. Inother words, the UE neural network manager 218 obtains each NN formationconfiguration from a neural network table (e.g., neural network table216) using the index values, and forms at least one candidate deepneural network using a set of candidate NN formation configurationelements.

The UE neural network manager 218 then obtains one or more metricsassociated with a set of candidate deep neural networks and selects theneural network formation configuration based on the one or more metrics.Some implementations obtain a respective error metric for each candidateneural network formation configuration of the set of candidate neuralnetwork formation configurations to generate a set of error metrics. TheUE neural network manager then compares each error metric of the set ofthe error metrics to a threshold value, and selects the neural networkformation configuration based on the set of error metric. To illustrate,the UE neural network manager, in some examples, identifies a particularerror metric (e.g., CRC passes) in the set of error metrics thatindicates less error relative to other error metrics in the set of errormetrics, and selects, as the neural network formation configuration, aparticular candidate neural network formation configuration, from theset of candidate neural network formation configurations, thatcorresponds to the particular error metric. In an exampleimplementation, the UE neural network manager 218 analyzes eachcandidate DNN, such as by providing a known input 1634 to each candidateDNN and comparing the respective outputs from each candidate deep neuralnetwork (e.g., output 1636, output 1638, output 1640). Alternately oradditionally, the UE neural network manager provides other types ofinput, examples of which are provided herein.

To illustrate, consider an example in which each candidate DNNcorresponds to generating transmitter communications. The UE neuralnetwork manager can compare various transmission metrics of output 1636,output 1638, output 1640, such as output power level, modulation qualitymetrics, intermodulation product metrics, etc. As another example,consider a scenario in which each candidate DNN corresponds toprocessing receiver communications. The UE neural network manager canthen compare various receiver metrics of output 1636, output 1638,output 1640, such as CRC, Signal-to-Noise (SNR), adjacent/alternatechannel metrics, BER, Inter-symbol Interference (ISI) metrics, etc. TheUE neural network manager 218 then selects one of the candidate neuralnetwork formation configurations based on the analysis.

In the environment 1600-4, the UE 110 processes the communications 1642using a deep neural network 1644 (DNN 1644) formed with the selectedcandidate neural network formation configuration. In the environment1600-4, the DNN (which corresponds to the candidate DNN 1632) processesdownlink communications received from the base station 121, but inalternate implementations, the DNN 1644 processes uplink communications,such as by generating transmission communications that the UE 110transmits to the base station 121.

Consider now FIG. 17 that illustrates a fourth example of signaling andcontrol transactions for communicating neural network formationconfigurations, denoted in FIG. 17 by the signaling and controltransaction diagram 1700. In the fourth example, a base station providesthe UE with multiple candidate neural network formation configurations,and the UE selects one of the candidate neural network formationconfigurations to form a deep neural network that processescommunications. While the signaling and control transaction diagram 1700illustrates the base station 121 and the UE 110 of FIG. 1, alternateimplementations include a core network server, such as core networkserver 302 of FIG. 3 performing some or all of the functionalityperformed by the base station 121.

At 1705, the base station 121 determines a set of candidate neuralnetwork formation configurations. For example, the base station 121 orthe core network server 302 (using base station neural network manager268 of FIG. 2 or core network neural network manager 312 of FIG. 3)analyzes a neural network table to identify candidate neural networkformation configurations (e.g., sets of neural network formationconfiguration elements) linked to input characteristics within aspecified range and/or threshold. To illustrate, the base stationanalyzes the input characteristics to identify candidate neural networkformation configurations with operating conditions that fall within“X”+/−a threshold value. In identifying the candidate neural networkformation configurations, the base station alternately or additionallyidentifies the index value of each candidate neural network formationconfiguration element selected for the respective candidate neuralnetwork formation configuration, such as that described with referenceto the environment 1600-1 of FIG. 16-1 (e.g., NN formation configurationelement set 1602, NN formation configuration element set 1604, NNformation configuration element set 1606).

At 1710, the base station 121 transmits an indication of the set ofcandidate neural network formation configurations to the UE 110. Forexample, with reference to the environment 1600-2 of FIG. 16-1, the basestation transmits each index value associated with the set of candidateneural network formation configurations to the UE (e.g. a set of indexvalues corresponding to a set of neural network formation configurationsand/or configuration elements). In some cases, the base stationtransmits the index values in a single message, while in other cases,the base station transmits each index value in a respective message.

At 1715, the UE 110 analyzes the set of candidate neural networkformation configurations based on communications transmitted over thewireless communication system, such as communications transmitted by thebase station 121. In some implementations, the UE 110 processes thecommunications by forming a respective deep neural network with eachcandidate neural network formation configuration and processing thecommunications with each respective deep neural network, such as byextracting the respective NN formation configuration elements from aneural network table. As one example, the UE performs blindidentification of the neural network formation configuration byprocessing the communications using each of the respective deep neuralnetworks to attempt to decode expected data patterns within thecommunications, such as that described with reference to the environment1600-3 of FIG. 16-2. The UE then analyzes each respective deep neuralnetwork, such as by analyzing the outputs of each respective deep neuralnetwork and generating respective metric(s) (e.g., accuracy, bit errors,etc.).

At 1720, the UE 110 selects a candidate neural network formationconfiguration of the set of candidate neural network formationconfigurations. For instance, the UE selects the candidate neuralnetwork formation configuration that forms the respective deep neuralnetwork that decodes the expected data pattern with the least bit errorsrelative to other candidate neural network formation configurations. At1725, the UE 110 forms a deep neural network using the selectedcandidate neural network formation configuration, and processescommunications with the deep neural network, such as that described withreference to the environment 1600-4 of FIG. 16-2.

Having described an example environment, and signal and controltransactions, that can be used to communicate neural network formationconfigurations by using a set of candidate neural network formationconfigurations, consider now some example methods that are in accordancewith one or more implementations.

Example Methods

Example methods 1800 and 1900 are described with reference to FIG. 18and FIG. 19 in accordance with one or more aspects of communicatingneural network formation configurations. The order in which the methodblocks are described are not intended to be construed as a limitation,and any number of the described method blocks can be skipped or combinedin any order to implement a method or an alternate method. Generally,any of the components, modules, methods, and operations described hereincan be implemented using software, firmware, hardware (e.g., fixed logiccircuitry), manual processing, or any combination thereof. Someoperations of the example methods may be described in the generalcontext of executable instructions stored on computer-readable storagememory that is local and/or remote to a computer processing system, andimplementations can include software applications, programs, functions,and the like. Alternatively, or additionally, any of the functionalitydescribed herein can be performed, at least in part, by one or morehardware logic components, such as, and without limitation,Field-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip systems (SoCs), Complex Programmable Logic Devices(CPLDs), and the like.

FIG. 18 illustrates an example method 1800 for communicating a neuralnetwork formation configuration for processing communicationstransmitted over a wireless communication system. In someimplementations, operations of the method 1800 are performed by anetwork entity, such as one of the base stations 120 and/or the corenetwork server 302.

At 1805, the network entity transmits a neural network table thatincludes a plurality of neural network formation configuration elements,where each neural network formation configuration element of theplurality of neural network formation configuration elements configuresat least a portion of a deep neural network for processingcommunications transmitted over a wireless communication system. Forexample, the network entity (e.g., base station 121) transmits theneural network table (e.g., neural network table 272, neural networktable 316) to the UE (e.g., UE 110) using a broadcast or multicastmessage to a group of UEs. As another example, the network entity (e.g.,base station 121) transmits the neural network table to the UE (e.g., UE110) using a UE-dedicated message. In some implementations, the networkentity (e.g., core network server 302) communicates the neural networktable to a base station (e.g., base station 121), and directs the basestation to transmit the neural network table to the UE (e.g., UE 110).At times, the network entity transmits the neural network table usinglayer 3 messaging. Alternately or additionally, the network entitytransmits multiple neural network tables, where each neural networktable has a designated processing assignment as further described.

At 1810, the network entity selects one or more neural network formationconfiguration elements from the plurality of neural network formationconfiguration elements to create a neural network formationconfiguration. For instance, the network entity (e.g., base station 121,core network server 302) selects the neural network formationconfiguration elements by comparing current operating condition(s) withinput characteristics stored in the neural network table, and selectingthe neural network formation configuration elements by correlating ormatching the input characteristics to the current operating conditions.In some implementations, the network entity selects a set of candidateneural network formation configurations.

At 1815, the network entity transmits an indication to a user equipmentto direct the user equipment to form a deep neural network using theneural network formation configuration and to process communicationsusing the deep neural network. As one example, the network entity (e.g.,base station 121) determines index value(s) of the neural network tablethat corresponds to the neural network formation configuration and/or aset of neural network formation configuration elements, and transmitsthe index value(s) to UE 110, such as by transmitting the index value(s)using a downlink control channel, by transmitting the index value(s) inlayer 2 message(s), etc. As another example, the network entity (e.g.,core network server 302) communicates an indication to a base station(e.g., base station 121), and directs the base station to transmit theindication to the user equipment (e.g., UE 110). The network entityalternately or additionally indicates a processing assignment for thedeep neural network formed with the neural network formationconfiguration, such as by indicating a processing assignment thatdirects the user equipment to process downlink control channelcommunications with the deep neural network. In some implementations,the network entity indicates a set of candidate neural network formationconfigurations, such as that described with reference to FIGS. 16-1 and16-2.

In transmitting the index value(s), the network entity sometimesspecifies rule(s) on when to process communications with thecorresponding deep neural network. As one example, the network entity(e.g., base station 121, core network server 302) determines a timethreshold value between data channel communications and control channelcommunications. When transmitting the index value to direct the userequipment (e.g., UE 110) to form a deep neural network, the networkentity transmits the time value threshold and a rule that directs theuser equipment to form the deep neural network using a default formationconfiguration when a timing between the data channel communications andthe control channel communications is below the time threshold value.Additionally or alternatively, a rule may direct the user equipment touse the same neural network formation configuration for data channelcommunications and control channel communications when a data channeland control channel are within a time threshold value and so there isnot enough time to switch between different DNNs.

FIG. 19 illustrates an example method 1900 for communicating a neuralnetwork formation configuration for processing communicationstransmitted over a wireless communication system, such as by processinga set of candidate neural network formation configurations and selectinga particular candidate neural network formation configuration asdescribed with reference to FIG. 17. In some implementations, operationsof the method 1900 are performed by a user equipment, such as UE 110.

At 1905, a user equipment receives a neural network table that includesa plurality of neural network formation configuration elements thatprovide a user equipment with an ability to configure a deep neuralnetwork for processing communications transmitted over a wirelesscommunication system. The user equipment (e.g., UE 110), for example,receives the neural network table (e.g., neural network table 272) froma base station (e.g., base station 121) in layer 3 messaging. As anotherexample, the user equipment receives the neural network table in amulticast or broadcast message. Alternately or additionally, the userequipment receives the neural network table in a UE-dedicated message.In some cases, the user equipment receives multiple neural networktables, where each neural network table has a designated processingassignment.

At 1910, the user equipment receives a message that directs the userequipment to form the deep neural network using a neural networkformation configuration based on one or more neural network formationconfiguration elements in the plurality of neural network formationconfiguration elements. For example, the user equipment (e.g., UE 110)receives, in the message, a set of index values to the neural networktable, where the set of index values corresponds to a set of candidateneural network formation configurations and/or configuration elements,such as the communication 1626 in the environment 1600-2 of FIG. 16-1,the indication transmitted by base station 121 at 1710 of FIG. 17, etc.In implementations, the user equipment (e.g., UE 110) receives adownlink control channel message that includes index value(s)corresponding to an entry (or entries) in the neural network table. Insome implementations, the message includes an indication of a processingassignment for a deep neural network formed with the neural networkformation configuration, such as a communication channel processingassignment, a processing chain processing assignment, etc.

At 1915, the user equipment forms the deep neural network with theneural network formation configuration by accessing the neural networktable to obtain the neural network formation configuration elements. Inone example, the user equipment (e.g., UE 110) accesses the neuralnetwork table using the index value(s). Alternately or additionally, theuser equipment obtains the set of candidate neural network formationconfigurations by accessing the neural network table using a sets ofindex value(s), and forms candidate deep neural networks using eachcandidate neural network formation configuration, such as that describedwith reference to the environment 1600-2 of FIG. 16-1 and theenvironment 1600-3 of FIG. 16-2. The UE 110, for instance, forms arespective candidate deep neural network using each candidate neuralnetwork formation configuration and/or configuration elements, andprocesses the communications with each candidate respective deep neuralnetwork to obtain respective metrics about each candidate deep neuralnetwork as described at 1715 of FIG. 17, and selects the neural networkformation configuration based on the one or more metrics, such as thatdescribed at 1720 of FIG. 17.

As one example, the user equipment (e.g., UE 110) obtains an errormetric for each candidate neural network formation configuration of theset of candidate neural network formation configurations to generate aset of error metrics, such as CRC metrics. The user equipment compareseach CRC metric of the set of the CRC metrics to a threshold value. Inturn, the user equipment identifies a particular CRC metric in the setof CRC metrics that exceeds the threshold value, and selects, as theneural network formation configuration, a particular candidate neuralnetwork formation configuration, from the set of candidate neuralnetwork formation configurations, that corresponds to the particular CRCmetric. While described in the context of a CRC metric, other errormetrics can be utilized as well, such as BER, ARQ, HARQ, Frame ErrorRate (FER), etc.

In some implementations, when forming the deep neural network, the userequipment (e.g., UE 110) determines to process a first communicationchannel using a first deep neural network formed with the neural networkformation configuration, and identifies a second communication channelthat is quasi-correspondent to the first communication channel. Inresponse to identifying the second communication channel isquasi-correspondent, the user equipment (e.g., UE 110) determines toprocess the second communication channel using a second deep neuralnetwork formed with the neural network formation configuration.

In response to forming the deep neural network, the user equipmentprocesses the communications transmitted over the wireless communicationsystem using the deep neural network at 1920. For example, the userequipment (e.g., UE 110), processes a downlink communication channelusing the deep neural network.

Although aspects of base station-user equipment messaging regardingneural networks, and communicating neural network formationconfigurations, have been described in language specific to featuresand/or methods, the subject of the appended claims is not necessarilylimited to the specific features or methods described. Rather, thespecific features and methods are disclosed as example implementationsof base station-user equipment messaging regarding neural networks, andcommunicating neural network formation configurations, and otherequivalent features and methods are intended to be within the scope ofthe appended claims. Further, various different aspects are described,and it is to be appreciated that each described aspect can beimplemented independently or in connection with one or more otherdescribed aspects.

In the following, several examples are described.

Example 1: A method performed by a network entity associated with awireless communication system, the method comprising: determining aneural network formation configuration for a deep neural network forprocessing communications transmitted over the wireless communicationsystem; generating a message that includes an indication of the neuralnetwork formation configuration for the deep neural network; andtransmitting the message to a user equipment to direct the userequipment to form the deep neural network using the neural networkformation configuration and to process the communications transmittedover the wireless communication system using the deep neural network.

Example 2: The method as recited in example 1, wherein the generatingthe message further comprises including a processing assignment for thedeep neural network in the message.

Example 3: The method as recited in either example 1 or example 2,wherein the generating the message further comprises specifying, in themessage, a time instance that indicates a time to start using the deepneural network with the neural network formation configuration forprocessing the communications.

Example 4: The method as recited in any one of the preceding examples,wherein the neural network formation configuration comprises a firstneural network formation configuration, the indication comprises a firstindication, the message comprises a first message, and the methodfurther comprises: receiving feedback from the user equipment thatprovides one or more metrics associated with the communications;determining a second neural network formation configuration for the deepneural network based, at least in part, on the feedback; generating asecond message that includes a second indication of the second neuralnetwork formation configuration for the deep neural network; andtransmitting the second message to the user equipment to direct the userequipment to update the deep neural network with the second neuralnetwork formation configuration.

Example 5: The method as recited in example 4, wherein the second neuralnetwork formation configuration comprises a delta neural networkformation configuration.

Example 6: The method as recited any one of the preceding examples,wherein the determining the neural network formation configurationcomprises determining the neural network formation configuration based,at least in part, on scheduling Multiple User, Multiple-Input, MultipleOutput downlink transmissions in the wireless communication system.

Example 7: The method as recited in any one of examples 1 to 6, whereinthe neural network formation configuration comprises a first neuralnetwork formation configuration, the deep neural network comprises afirst deep neural network, the communications comprise downlinkcommunications, and the method further comprises: determining a secondneural network formation configuration for a second deep neural networkfor processing uplink communications from the user equipment; andtransmitting the second neural network formation configuration to theuser equipment and directing the user equipment to form the second deepneural network using the second neural network formation configuration,and to process the uplink communications using the second deep neuralnetwork.

Example 8: The method as recited in example 7, wherein the transmittingthe message to direct the user equipment to form the deep neural networkusing the first neural network formation configuration comprisestransmitting the message using a first radio access technology, andwherein the transmitting the second neural network formationconfiguration comprises transmitting the second neural network formationconfiguration using a second radio access technology that is differentfrom the first radio access technology.

Example 9: The method as recited in any one of examples 1 to 6, whereinthe neural network formation configuration comprises a first neuralnetwork formation configuration, the deep neural network comprises afirst deep neural network, the communications comprise downlinkcommunications, and the method further comprises: forming a second deepneural network; and processing the uplink communications using thesecond deep neural network to decode the uplink communications.

Example 10: The method as recited in any one of examples 1 to 6, whereinthe neural network formation configuration comprises a first neuralnetwork formation configuration, the deep neural network comprises afirst deep neural network, the communications comprise downlinkcommunications, and the method further comprises: forming a second deepneural network; and processing the downlink communications using thesecond deep neural network to encode the downlink communications.

Example 11: The method as recited in any one of the preceding examples,wherein the message comprises a first message, and wherein thedetermining the neural network formation configuration for processingthe communications comprises: receiving, from the user equipment, asecond message that indicates one or more capabilities of the userequipment; and determining the neural network formation configurationbased, at least in part, on the one or more capabilities of the userequipment.

Example 12: A method performed by a user equipment associated with awireless communication system, the method comprising: receiving, by theuser equipment, a message that indicates a neural network formationconfiguration for a deep neural network for processing communicationsassociated with the wireless communication system; forming the deepneural network using the neural network formation configurationindicated in the message; receiving the communications from a basestation; and processing the communications using the deep neural networkto extract information in the communications.

Example 13: The method as recited in example 12, wherein the receivingthe message that indicates the neural network formation configurationcomprises: receiving a broadcast message from the base station; andextracting the neural network formation configuration from the broadcastmessage.

Example 14: The method as recited in either example 12 or example 13,wherein the forming the deep neural network using the neural networkformation configuration comprises extracting, from the message, a timeinstance that indicates a time to start using the neural networkformation configuration to process the communications, and wherein theprocessing the communications using the deep neural network comprisesprocessing the communications using the deep neural network based on thetime indicated by the time instance.

Example 15: The method as recited in any one of examples 12 to 14,wherein the message comprises a first message, the deep neural networkcomprises a first deep neural network, the communications comprisedownlink communications, the processing the communications comprisesusing the first deep neural network to decode information from thedownlink communications, and the method further comprises: receiving, bythe user equipment, a second message that indicates a second neuralnetwork formation configuration for a second deep neural network forprocessing uplink communications transmitted over the wirelesscommunication system; forming the second deep neural network using thesecond neural network formation configuration; and processing the uplinkcommunications using the second deep neural network to encodeinformation on the uplink communications.

Example 16: The method as recited in any one of the preceding examples,wherein the neural network formation configuration comprises a firstneural network formation configuration, the message comprises a firstmessage, the indication comprises a first indication, and the methodfurther comprises: receiving a second message that includes a secondindication of a second neural network formation configuration for thedeep neural network; and forming the deep neural network using thesecond neural network formation configuration.

Example 17: The method as recited in example 16, wherein the secondneural network formation configuration comprises a delta neural networkformation configuration.

Example 18: The method as recited in any one of examples 11 to 17,wherein the receiving the message that indicates the neural networkformation configuration comprises receiving, in the message, anindication of a processing assignment of the deep neural network,wherein the forming the deep neural network comprises forming the deepneural network in a communications processing chain as specified in theprocessing assignment.

Example 19: A network entity comprising: a wireless transceiver; aprocessor; and computer-readable storage media comprising instructionsto implement a deep neural network manager module that, responsive toexecution by the processor, directs the network entity to perform anyone of the methods of examples 1 to 11.

Example 20: A user equipment comprising: a wireless transceiver; aprocessor; and computer-readable storage media comprising instructionsto implement a deep neural network manager module that, responsive toexecution by the processor, directs the user equipment to perform anyone of the methods of examples 12 to 18.

Example 21: A method performed by a network entity associated with awireless communication system, the method comprising: transmitting, bythe network entity and to a user equipment, a neural network table thatincludes a plurality of neural network formation configuration elements,each neural network formation configuration element of the plurality ofneural network formation configuration elements configuring at least aportion of a deep neural network for processing communicationstransmitted over the wireless communication system; selecting one ormore neural network formation configuration elements from the pluralityof neural network formation configuration elements to create a neuralnetwork formation configuration; and transmitting an indication to theuser equipment to direct the user equipment to form a deep neuralnetwork using the neural network formation configuration and to processthe communications using the deep neural network.

Example 22: The method as recited in example 21, wherein thetransmitting the indication comprises transmitting at least one indexvalue that maps to the one or more neural network formationconfiguration elements in the neural network table.

Example 23: The method as recited in example 1, wherein the selectingthe neural network formation configuration comprises selecting a set ofcandidate neural network formation configuration elements, and whereintransmitting the indication comprises transmitting an indication of theset of candidate neural network formation configuration elements.

Example 24: The method as recited in example 3, wherein the transmittingthe indication of the set of candidate neural network formationconfiguration elements comprises transmitting a set of index values thatmap to entries in the neural network table.

Example 25: The method as recited in any one of the preceding examples,wherein the indication comprises a first indication, the deep neuralnetwork comprises a first deep neural network, the neural networkformation configuration comprises a first neural network formationconfiguration, and the method further comprises: directing the userequipment to process control channel communications transmitted over thewireless communication system using the first deep neural network;determining a second neural network formation configuration; andtransmitting a second indication to the user equipment to direct theuser equipment to form a second deep neural network using the secondneural network formation configuration and to process data channelcommunications transmitted over the wireless communication system usingthe second deep neural network.

Example 26: The method as recited in example 25, further comprising:determining a time threshold value between the data channelcommunications and the control channel communications; and directing theuser equipment to form the second deep neural network using a defaultformation configuration when a timing between the data channelcommunications and the control channel communications is below the timethreshold value.

Example 27: The method as recited in any one of the preceding examples,wherein the transmitting the neural network table comprises transmittingthe neural network table in a multicast message.

Example 28: The method as recited in any one of the preceding examples,wherein the neural network formation configuration comprises a firstneural network formation configuration, the indication comprises a firstindication, and the method further comprises: receiving feedback fromthe user equipment that provides one or more metrics associated with thecommunications; determining a second neural network formationconfiguration for the deep neural network based, at least in part, onthe feedback; and transmitting a second indication to the user equipmentto direct the user equipment to update the deep neural network with thesecond neural network formation configuration.

Example 29: The method as recited in example 28, wherein the secondneural network formation configuration comprises a delta neural networkformation configuration.

Example 30: A method performed by a user equipment associated with awireless communication system, the method comprising: receiving, by theuser equipment, a neural network table that includes a plurality ofneural network formation configuration elements that provide the userequipment with an ability to configure a deep neural network forprocessing communications transmitted over the wireless communicationsystem; receiving, by the user equipment, a message that directs theuser equipment to form the deep neural network using a neural networkformation configuration based on one or more neural network formationconfiguration elements in the plurality of neural network formationconfiguration elements; forming the deep neural network with the neuralnetwork formation configuration by accessing the neural network table toobtain the one or more neural network formation configuration elements;and processing the communications transmitted over the wirelesscommunication system using the deep neural network.

Example 31: The method as recited in example 30, wherein the receivingthe message that directs the user equipment to form the deep neuralnetwork using the neural network formation configuration furthercomprises: extracting a processing assignment from the message; andwherein the forming the deep neural network with the neural networkformation configuration comprises forming the deep neural network in acommunications processing chain as specified in the processingassignment.

Example 32: The method as recited in example 30 or example 31, whereinthe receiving the message that directs the user equipment to form thedeep neural network using the neural network formation configurationfurther comprises receiving at least one index value that maps to anentry in the neural network table, and wherein the forming the deepneural network with the neural network formation configuration comprisesaccessing the neural network table using the index value.

Example 33: The method as recited in any one of the preceding examples,wherein the deep neural network comprises a first deep neural network,and wherein the forming the deep neural network with the neural networkformation configuration further comprises: determining to process afirst communication channel by using the first deep neural networkformed with the neural network formation configuration; identifying asecond communication channel is quasi-correspondent to the firstcommunication channel; and determining, based on the identifying, toprocess the second communication channel using a second deep neuralnetwork formed with the neural network formation configuration.

Example 34: The method as recited in example 33, wherein the identifyingthe second communication channel is quasi-correspondent to the firstcommunication channel comprises receiving an indication that the secondcommunication channel and the first communication channel arequasi-correspondent.

Example 35: The method as recited in example 30, wherein the receivingthe message that directs the user equipment to form the deep neuralnetwork using the neural network formation configuration furthercomprises receiving, by the user equipment, a set of index values thatcorrespond to candidate neural network formation configuration elementsto use as the neural network formation configuration, and wherein theforming the deep neural network with the neural network formationconfiguration by accessing the neural network table to obtain the neuralnetwork formation configuration comprises: accessing the neural networktable using the set of index values to obtain the candidate neuralnetwork formation configuration elements; forming a set of candidatedeep neural networks, using the candidate neural network formationconfiguration elements, to process the communications; obtaining one ormore metrics associated with the set of candidate deep neural networks;and selecting the neural network formation configuration based on theone or more metrics.

Example 36: The method as recited in example 35, wherein the obtainingthe one or more metrics comprises: obtaining a respective error metricfor each candidate deep neural network of the set of candidate deepneural networks to generate a set of error metrics; and comparing eacherror metric of the set of the error metrics to a threshold value, andwherein selecting the neural network formation configuration comprises:identifying a particular error metric in the set of error metrics thatindicates less error relative to other error metrics in the set of errormetrics; and selecting, as the neural network formation configuration, aparticular candidate deep neural network, from the set of candidate deepneural networks, that corresponds to the particular error metric.

Example 37: The method as recited in any one of the preceding examples,wherein the message comprises a first message, the deep neural networkcomprises a first deep neural network, the neural network formationconfiguration comprises a first neural network formation configuration,the first message includes a first processing assignment that directsthe user equipment to process control channel communications using thefirst deep neural network, and the method further comprises: receiving asecond message that directs the user equipment to form a second deepneural network using a second neural network formation configuration,the second message including a second processing assignment for thesecond deep neural network; forming the second deep neural network withthe second neural network formation by accessing the neural networktable to obtain the second neural network formation configuration; andprocessing, based on the processing assignment, data channelcommunications transmitted over the wireless communication system usingthe second deep neural network.

Example 38: The method as recited in any one of the preceding examples,wherein the receiving the neural network table comprises receiving theneural network table in a multicast message.

Example 39: A network entity comprising: a wireless transceiver; aprocessor; and computer-readable storage media comprising instructionsto implement a deep neural network manager module that, responsive toexecution by the processor, directs network entity to perform any one ofthe methods of examples 21 to 29.

Example 40: A user equipment comprising: a wireless transceiver; aprocessor; and computer-readable storage media comprising instructions,responsive to execution by the processor, for directing the userequipment to perform one of the methods of examples 30 to 38.

1. A method performed by a network entity associated with a wireless communication system, the method comprising: determining a neural network formation configuration for a deep neural network for processing communications transmitted over the wireless communication system; generating a message that includes an indication of the neural network formation configuration for the deep neural network; and transmitting the message to a user equipment to direct the user equipment to form the deep neural network using the neural network formation configuration and to process the communications transmitted over the wireless communication system using the deep neural network.
 2. The method as recited in claim 1, wherein the generating the message further comprises including a processing assignment for the deep neural network in the message.
 3. The method as recited in claim 1, wherein the generating the message further comprises specifying, in the message, a time instance that indicates a time to start using the deep neural network with the neural network formation configuration for processing the communications.
 4. The method as recited in claim 1, wherein the neural network formation configuration comprises a first neural network formation configuration, the indication comprises a first indication, the message comprises a first message, and the method further comprises: receiving feedback from the user equipment that provides one or more metrics associated with the communications; determining a second neural network formation configuration for the deep neural network based, at least in part, on the feedback; generating a second message that includes a second indication of the second neural network formation configuration for the deep neural network; and transmitting the second message to the user equipment to direct the user equipment to update the deep neural network with the second neural network formation configuration.
 5. The method as recited in claim 4, wherein the second neural network formation configuration comprises a delta neural network formation configuration.
 6. The method as recited in claim 1, wherein the determining the neural network formation configuration comprises determining the neural network formation configuration based, at least in part, on scheduling Multiple User, Multiple-Input, Multiple Output downlink transmissions in the wireless communication system.
 7. The method as recited in claim 1, wherein the neural network formation configuration comprises a first neural network formation configuration, the deep neural network comprises a first deep neural network, the communications comprise downlink communications, and the method further comprises: determining a second neural network formation configuration for a second deep neural network for processing uplink communications from the user equipment; and transmitting the second neural network formation configuration to the user equipment and directing the user equipment to form the second deep neural network using the second neural network formation configuration, and to process the uplink communications using the second deep neural network.
 8. The method as recited in claim 7, wherein the transmitting the message to direct the user equipment to form the deep neural network using the first neural network formation configuration comprises transmitting the message using a first radio access technology, and wherein the transmitting the second neural network formation configuration comprises transmitting the second neural network formation configuration using a second radio access technology that is different from the first radio access technology.
 9. (canceled)
 10. (canceled)
 11. The method as recited in claim 1, wherein the message comprises a first message, and wherein the determining the neural network formation configuration for processing the communications comprises: receiving, from the user equipment, a second message that indicates one or more capabilities of the user equipment; and determining the neural network formation configuration based, at least in part, on the one or more capabilities of the user equipment.
 12. A method performed by a user equipment associated with a wireless communication system, the method comprising: receiving, by the user equipment, a message that indicates a neural network formation configuration for a deep neural network for processing communications associated with the wireless communication system; forming the deep neural network using the neural network formation configuration indicated in the message; receiving the communications from a base station; and processing the communications using the deep neural network to extract information in the communications.
 13. The method as recited in claim 12, wherein the receiving the message that indicates the neural network formation configuration comprises: receiving a broadcast message from the base station; and extracting the neural network formation configuration from the broadcast message.
 14. The method as recited in claim 12, wherein the forming the deep neural network using the neural network formation configuration comprises extracting, from the message, a time instance that indicates a time to start using the neural network formation configuration to process the communications, and wherein the processing the communications using the deep neural network comprises processing the communications using the deep neural network based on the time indicated by the time instance.
 15. The method as recited in claim 12, wherein the message comprises a first message, the deep neural network comprises a first deep neural network, the communications comprise downlink communications, the processing the communications comprises using the first deep neural network to decode information from the downlink communications, and the method further comprises: receiving, by the user equipment, a second message that indicates a second neural network formation configuration for a second deep neural network for processing uplink communications transmitted over the wireless communication system; forming the second deep neural network using the second neural network formation configuration; and processing the uplink communications using the second deep neural network to encode information on the uplink communications.
 16. The method as recited in claim 12, wherein the neural network formation configuration comprises a first neural network formation configuration, the message comprises a first message, and the method further comprises: receiving a second message that indicates a second neural network formation configuration for the deep neural network; and forming the deep neural network using the second neural network formation configuration.
 17. The method as recited in claim 16, wherein the second neural network formation configuration comprises a delta neural network formation configuration.
 18. The method as recited in claim 12, wherein the receiving the message that indicates the neural network formation configuration comprises receiving, in the message, an indication of a processing assignment of the deep neural network, wherein the forming the deep neural network comprises forming the deep neural network in a communications processing chain as specified in the processing assignment.
 19. A network entity comprising: a wireless transceiver; a processor; and computer-readable storage media comprising instructions to implement a deep neural network manager module that, responsive to execution by the processor, directs the network entity to perform operations comprising: determining a neural network formation configuration for a deep neural network for processing communications transmitted over a wireless communication system; generating a message that includes an indication of the neural network formation configuration for the deep neural network; and transmitting the message to a user equipment to direct the user equipment to form the deep neural network using the neural network formation configuration and to process the communications transmitted over the wireless communication system using the deep neural network.
 20. (canceled)
 21. The network entity as recited in claim 19, wherein the computer-readable storage media comprises further instructions that direct the network entity to perform transmitting the indication by: transmitting at least one index value that maps to the neural network formation configuration in a neural network table.
 22. The network entity as recited in claim 21, wherein the computer-readable storage media comprises further instructions that direct the network entity to perform further operations comprising: transmitting, by the network entity and to a user equipment, the neural network table by transmitting a plurality of neural network formation configuration elements, each neural network formation configuration element of the plurality of neural network formation configuration elements configuring at least a portion of a deep neural network for processing communications transmitted over the wireless communication system.
 23. The network entity as recited in claim 19, wherein transmitting the message further comprises: transmitting the message that includes the indication of the neural network formation configuration in one or more broadcast messages. 